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Custom Tokenizer with Hugging Face Transformers, Multi-Agent Chat Application Using LangGraph @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Live Webinar: The Power of Data Storytelling in Driving Business Decisions (September 10, 2024 at 9 AM CST)Data doesn’t have to be overwhelming. Join our webinar to learn about Data Storytelling and turn complex information into actionable insights for faster decision-making.Click below to check the schedule in your time zone and secure your spot. Can't make it? Register to get the recording instead.REGISTER FOR FREESponsoredHappy Friday! 🌟Welcome to DataPro #110—Your Ultimate Data Science & ML Update! 🚀In the world of AI and ML, sharp reasoning is the key to smarter decisions and impactful leadership. Our latest insights and strategies will help you boost model accuracy, optimize performance, and cut costs with scalable solutions. Dive in for cutting-edge tips and real-world techniques to elevate your data game.📚 Book Haven: Top Reads & Author Insights◽"Data Science for Decision Makers": Elevate your leadership with data science and AI prowess by Jon Howells.◽"Data Science for IoT Engineers": Unlock data science techniques and ML applications for innovative IoT solutions by P. G. Madhavan.◽"Bash for Data Scientists": Master shell scripting for data science tasks with Oswald Campesato.◽"Angular and Machine Learning Pocket Primer": Get the essentials on integrating ML with Angular, also by Oswald Campesato.◽"AI, ML, and Deep Learning": Explore advanced AI techniques with Oswald Campesato’s practical guide.🔍 Model Breakdown: Algorithm of the Week◽Custom Tokenizers for Non-English Languages: Dive into Hugging Face Transformers for multilingual models.◽Concrete ML Privacy: Secure end-to-end privacy in model training and inference.◽Multilingual Multi-Agent Chat with LangGraph: Build diverse language chat applications.◽Approximating Stochastic Functions: Techniques for multivariate output functions.🪐Trendspotting: Hot Tech Trends◽Legal Reasoning Engines: How reasoning drives legal arguments.◽R Clinical Flowcharts with shinyCyJS: Use R for clinical flowcharting.◽Claude for Enterprise: Explore Anthropic's latest.◽IBM Quantum Update: Qiskit SDK v1.2 release news!🛠️ Platform Showdown: ML Tools & Services◽FastAPI for ML Web Apps: Build powerful web apps with FastAPI.◽DetoxBench: Benchmarking large language models for fraud and abuse detection.◽Llama-3.1-Storm-8B & CausalLM/miniG: New Hugging Face models.◽Build RAG Pipelines: Combine LlamaIndex with Amazon Bedrock for robust pipelines.📊 Success Stories: ML in Action◽Ecommerce Data Quality: Strategies for improving data quality.◽Essential Python Modules: Must-know Python modules for data engineers.◽Avoiding Data Science Mistakes: Tips to steer clear of common pitfalls.◽Thomson Reuters Labs: Accelerating AI/ML innovation with AWS MLOps.◽Galxe & AlloyDB: Cost-cutting success story.🌍 ML Newsflash: Industry Buzz & Discoveries◽GPT-4 for Customer Service: Redefining standards with GPT-4.◽HYGENE: A novel diffusion-based hypergraph generation method.◽Yi-Coder: Meet a compact yet powerful LLM for code.◽Guided Reasoning: New approaches to enhance multi-agent system intelligence.Enjoy the newsletter and have a fantastic weekend! ✨DataPro Newsletter is not just a publication; it’s a complete toolkit for anyone serious about mastering the ever-changing landscape of data and AI. Grab your copyand start transforming your data expertise today!Calling Data & ML Enthusiasts!Want to share your insights and build your online reputation? Contribute to our new Packt DataPro column! Discuss tools, share experiences, or ask questions. Gain recognition among 128,000+ data professionals and boost your CV. Simply reply with your Google Docs link or use our feedback form. Whether you’re looking for visibility or a discreet approach, we’re here to support you.Share your content today and engage with our vibrant community! We’re excited to hear from you!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬200+ hours of research on AI-led career growth strategies & hacks packed in 3 hoursThe only AI Crash Course you need to master 20+ AI tools, multiple hacks & prompting techniques in just 3 hoursYou’ll save 16 hours every week & find remote jobs using AI that will pay you upto $10,000/moRegister & save your seat now (100 free seats only)Sponsored📚 Book Haven: Must-Reads & Author InsightsDid you know? “Books are the quietest, most constant friends, holding the world’s treasured wisdom. They offer gentle guidance and timeless lessons, passing their rich inheritance from one generation to the next.”We’re thrilled to bring you this week’s must-have new releases, straight from the experts to your bookshelf! Whether you're eager to enhance your skills or explore new horizons, now is the perfect moment to add these invaluable resources to your collection.For a limited time,enjoy 30% off all eBooks at Packtpub.com. These books are thoughtfully crafted by industry insiders with hands-on experience, offering unique insights you won’t find anywhere else.Don’t let these Packt-exclusive deals slip away—seize the opportunity to learn from the best at an unbeatable price!Order Today at $24.99 $35.99Data Science for Decision Makers: Enhance your leadership skills with data science and AI expertiseBy Jon HowellsStruggling to bridge the gap between data science and business leadership? Our new book is here to help!What you’ll gain:✔️ Master statistics and ML to interpret models and drive decisions.✔️ Identify AI opportunities and oversee data projects from start to finish.✔️ Empower teams to tackle complex problems and build AI solutions.Elevate your leadership and make data work for you! Get the book now—just $24.99, down from $35.99!Order Today at $34.98$49.99Data Science for IoT Engineers: Master Data Science Techniques and Machine Learning Applications for Innovative IoT SolutionsBy Mercury Learning and Information, P. G. MadhavanDive into our new book, crafted for engineers, physicists, and mathematicians eager to bridge the gap between theory and practice!What’s inside:✔️ Integrate systems theory and machine learning seamlessly.✔️ Apply practical solutions like digital twins to real-world problems.✔️ Progress from basics to advanced techniques with ease.Whether you're tackling IoT challenges or modeling complex systems, this workbook with MATLAB code will guide you every step of the way. Get the eBook now for just $34.98, down from $49.99! Elevate your skills and tackle IoT and complex systems with confidence.Order Today at $37.99$54.99Bash for Data Scientists: A Comprehensive Guide to Shell Scripting for Data Science TasksBy Mercury Learning and Information, Oswald CampesatoUnlock the power of Bash for your data science projects with our latest book!What’s inside:✔️ Master Bash for efficient data processing with practical, real-world examples.✔️ Learn to integrate with Pandas and databases for advanced data handling.✔️ Get hands-on with grep, sed, and awk to clean and manage datasets effectively.Grab the eBook now for just $37.99, originally $54.99! Elevate your scripting skills and streamline your data tasks today!Order Today at $27.98$39.99Angular and Machine Learning Pocket Primer: A Comprehensive Guide to Angular and Integrating Machine LearningBy Mercury Learning and Information, Oswald CampesatoReady to elevate your Angular apps with machine learning? Our latest Pocket Primer has you covered!What’s inside:✔️ Seamless integration of Angular and machine learning using TensorFlow.js and Keras.✔️ Practical, step-by-step tutorials and real-world examples.✔️ Comprehensive coverage of Angular basics, UI development, and machine learning models.Get the eBook now for just $27.98, originally $39.99! Transform your skills and build sophisticated applications with ease.Order Today at $41.98$59.99Artificial Intelligence, Machine Learning, and Deep Learning: A Practical Guide to Advanced AI TechniquesBy Mercury Learning and Information, Oswald CampesatoDiscover the world of AI with our new book, perfect for expanding your skills from basics to advanced techniques!What’s inside:✔️ In-depth coverage of AI, machine learning, and deep learning.✔️ Practical examples and hands-on tutorials with Keras, TensorFlow, and Pandas.✔️ Explore classifiers, deep learning architectures, NLP, and reinforcement learning.Get the eBook now for just $41.98, down from $59.99! Transform your understanding and apply these cutting-edge concepts in real-world scenarios.🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ How to Create a Custom Tokenizer for Non-English Languages with Hugging Face Transformers? This blog explains the importance of tokenization in NLP and provides a detailed guide on training a custom tokenizer for non-English languages using Hugging Face libraries, ensuring improved model performance for diverse datasets.➽ End-to-end privacy for model training and inference with Concrete ML: This blog explores how to achieve end-to-end privacy in collaborative machine learning using federated learning and fully homomorphic encryption (FHE). It details a demo with scikit-learn and Concrete ML for secure model training and inference.➽ Building a Multilingual Multi-Agent Chat Application Using LangGraph: This blog details the development of a multilingual chat application to bridge language barriers in workplaces. It covers building features using LangChain and LangGraph, including agent design, translation workflows, and deployment with FastAPI.➽ Approximating Stochastic Functions with Multivariate Outputs: The article describes an enhanced method for training generative machine learning models, named Pin Movement Training (PMT). It extends the original PMT, which approximated single-output stochastic functions, to handle multiple-output functions. The approach uses a neural network and a hypersphere-based Z-space to map and approximate multidimensional outputs, like autoencoders but with uniform sampling for better results.Developing for iOS? Setapp's 2024 report on the state of the iOS market in the EU is a must-seeHow do users in the EU find apps? What's the main source of information about new apps? Would users install your app from a third-party app marketplace?Set yourself up for success with these and more valuable marketing insights in Setapp Mobile's report iOS Market Insights for EU.Get Insights freeSponsored🚀 Trendspotting: What's Next in Tech Trends➽ Reasoning as the Engine Driving Legal Arguments: The article explores how tribunals assess evidence in legal cases, focusing on three key stages: determining evidence relevance, evaluating trustworthiness, and weighing competing evidence. It highlights the role of "reasoning sentences" in explaining decision-making and discusses machine learning techniques for identifying these sentences in legal documents.➽ Use R to build Clinical Flowchart with shinyCyJS: The blog discusses creating Clinical Flowcharts for visualizing clinical trials, focusing on various methods, particularly using R. It details challenges and solutions in drawing flowcharts, including software limitations and customizations with shinyCyJS for precise visual representation.➽ Claude for Enterprise \ Anthropic: The Claude Enterprise plan now offers enhanced features for secure collaboration, including a 500K context window, GitHub integration, and advanced security measures. This allows teams to leverage internal knowledge while safeguarding data.➽ IBM Quantum Computing - Release news: Qiskit SDK v1.2 is here! Qiskit SDK v1.2 introduces major updates, including Rust-based circuit infrastructure for faster performance, improved synthesis and transpilation, and new features. It also ends support for Python 3.8, requiring Python 3.9 or later. 🛠️ Platform Showdown: Comparing ML Tools & Services➽ Using FastAPI for Building ML-Powered Web Apps: This tutorial demonstrates building a machine learning web app using FastAPI and Jinja2 templates. It covers creating a prediction API for a Random Forest model and integrating it with a web interface for user interaction.➽ DetoxBench: Benchmarking large language models for multitask fraud & abuse detection. This paper introduces a benchmark suite to evaluate large language models (LLMs) for detecting and mitigating fraud and abuse in various real-world scenarios, highlighting performance gaps and offering a tool for improving LLMs in high-stakes applications.➽ Llama-3.1-Storm-8B · Hugging Face: The Llama-3.1-Storm-8B model outperforms Meta’s Llama-3.1-8B-Instruct and Hermes-3 across multiple benchmarks. It improves instruction-following, QA, reasoning, and function-calling via self-curation, fine-tuning, and model merging techniques.➽ CausalLM/miniG · Hugging Face: The miniG model has two versions: standard and "alt," the latter trained with masked context to improve stability. Trained on a large dataset with text and image support, it performs best with Hugging Face Transformers for minimal performance degradation.➽ Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock: This blog explores using Retrieval Augmented Generation (RAG) techniques to enhance large language models (LLMs) by integrating external knowledge sources. It discusses building advanced RAG pipelines with LlamaIndex and Amazon Bedrock, covering topics like query routing, sub-question handling, and stateful agents.📊 Success Stories: Real-World ML Case Studies➽ Improving ecommerce data quality: This blog details how Lowe’s enhanced its website search accuracy by fine-tuning OpenAI’s GPT-3.5 model. By applying advanced prompt engineering, Lowe’s improved product data quality, reduced associate workload, and achieved a 20% accuracy boost in product tagging.➽ 10 Built-In Python Modules Every Data Engineer Should Know: This article highlights essential Python modules for data engineering, including tools for file management, data serialization, database interaction, and text processing. It covers how modules like `os`, `pathlib`, `shutil`, and `csv` can enhance data engineering tasks.➽ 5 Common Data Science Mistakes and How to Avoid Them: This blog outlines five common mistakes in data science projects, such as unclear objectives, neglecting basics, poor visualizations, lack of feature engineering, and overemphasizing accuracy. It offers practical solutions to avoid these pitfalls and improve project outcomes.➽ How Thomson Reuters Labs achieved AI/ML innovation at pace with AWS MLOps services? This post details how Thomson Reuters Labs developed a standardized MLOps framework using AWS SageMaker to streamline ML processes. It highlights the creation of TR MLTools and MLTools CLI to enhance efficiency, standardize practices, and accelerate AI/ML innovation.➽ Galxe migrates to AlloyDB for PostgreSQL, cutting costs by 40%: This blog explains how Galxe is addressing Web3 challenges by using AlloyDB for PostgreSQL and Google Cloud services. It highlights Galxe's innovations in decentralized identity, gamified user experiences, and scalable infrastructure to enhance Web3 adoption and performance.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ Using GPT-4 to deliver a new customer service standard: Ada, valued at $1.2B with $200M in funding, is leading a $100B shift in customer service with its AI-native automation platform. Since its 2016 inception, Ada has doubled resolution rates using OpenAI’s GPT-4, achieving up to 80% resolution and setting new industry standards for effectiveness.➽ HYGENE: A Diffusion-based Hypergraph Generation Method. The paper introduces HYGENE, a diffusion-based method for generating realistic hypergraphs. Using a bipartite representation, it iteratively expands nodes and hyperedges through a denoising process, effectively modeling complex hypergraph structures. This is the first deep learning approach for hypergraph generation.➽ Meet Yi-Coder: A Small but Mighty LLM for Code. Yi-Coder is an open-source series of coding-focused LLMs, available in 1.5B and 9B parameter sizes. It offers advanced coding performance with up to 128K token context modeling, surpassing models like CodeQwen1.5 and DeepSeek-Coder, and excels in benchmarks such as LiveCodeBench and HumanEval.➽ Guided Reasoning: A New Approach to Improving Multi-Agent System Intelligence. Gregor Betz from Logikon AI introduces Guided Reasoning, a multi-agent system where a guide agent helps client agents improve their reasoning through structured methods. This approach, using argument maps and pros/cons evaluations, aims to enhance clarity and accuracy in AI decision-making and explanations.See you next time! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

Google’s AI detective, Regnology Automates Ticket-to-Code with agentic GenAI on Vertex AI, MedFuzz @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Grow your business & career by 10x using AI Strategies in 4 hrs! 🤯Join GrowthSchool's AI Business Growth & Strategy Crash Course and discover how to revolutionise your approach to business on 12th September at 10 AM EST.In just 4 hours, you’ll gain the tools, insights, and strategies to not just survive, but dominate your market.This is more than just a workshop—it's a turning point.The first 100 to register get in for FREE. Don’t miss the chance to change your business trajectory forever.Sign up here to save your seat! 👈SponsoredWelcome to DataPro #111—Your Weekly Dose of Data Science & ML Magic! 🚀We’re now landing in your inbox every Thursday to keep you sharp and ahead of the game!In the ever-evolving realm of AI and ML, it's all about harnessing smart insights for impactful decisions and stellar leadership. Dive into our new Packt Signature Series, where you'll find expert tips on everything from real-time data management to mastering AI modeling. We’re here to equip you with the tools you need to navigate the data world like a pro.This week, we’ve got cutting-edge strategies to boost your model accuracy, optimize performance, and reduce costs with scalable solutions. Get ready for top-notch tips and practical techniques to supercharge your data skills.📚 Top Reads & Author Insights:✦ Building AI Intensive Python Applications:Dive deep into advanced AI apps.✦ Databricks ML in Action: Real-world applications and best practices.✦ Generative AI Application Integration Patterns:Innovative uses of generative AI.✦ Polars Cookbook:Essential recipes for efficient data handling.✦ Building LLM Powered Applications:Building with large language models.✦ Building Data-Driven Applications with LlamaIndex:Leveraging LlamaIndex for robust applications.✦ Data Quality in the Age of AI:Ensuring top-notch data quality.✦ Modern Computer Vision with PyTorch - Second Edition:Updated techniques in computer vision.✦ Accelerate Model Training with PyTorch 2.X:Speed up your model training.✦ Mastering PyTorch - Second Edition:The ultimate guide to mastering PyTorch.🔍 Algorithm Spotlight:✦ Apple’s Byte-Level ASR Optimization: A new AI algorithm for speech recognition.✦ IBM’s PowerLM-3B & PowerMoE-3B: Massive language models with advanced scheduling.✦ AtScale’s Open-Sourced SML: Transforming analytics with a new semantic modeling framework.✦ LG’s EXAONEPath: Enhancing histopathology analysis with a pre-trained model.🚀 Tech Trendwatch:✦ Tracing Memory Allocation in Python: Learn how to track memory usage.✦ Anomaly Detection in Streaming Data: Using Amazon Managed Service for Apache Flink.🛠️ ML Tool Showdown:✦7 Free Cloud IDEs You Need: Explore top IDEs for data science.✦ End-to-End Data Science Pipelines: From ingestion to visualization.✦ Sustainable MLOps: Optimizing operations for sustainability.📊 Success Stories:✦ GraphRAG’s Auto-Tuning: Adapting rapidly to new domains.✦ Enterprise Data Quality Guide: Navigating enterprise data challenges.✦ AI Agents for Daily Tasks: Automating routine app tasks.🌍 ML Newsflash:✦ Google’s AI Detective: Solving challenges with Gemini 1.5 Pro.✦ Regnology’s Gen AI on Vertex AI: Automating ticket-to-code processes.✦ MedFuzz on LLM Robustness: Evaluating LLMs in medical contexts.Stay tuned for your weekly dose of data brilliance! 🚀Take our weekly survey and get a free PDF copy of our best-selling book, "Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬📚 Packt Signature Series: Must-Reads & Author InsightsStep into a world of expert-driven knowledge with ourone-of-a-kindin-house content, crafted by industry pros to deliver the freshest insights on the latest tech releases. Discover how these cutting-edge titles are shaping the data landscape and unlocking the "whats," "hows," and "whys" behind emerging technologies. Whether you're looking to sharpen your skills or dive into something entirely new, there's never been a better time to expand your library with these essential resources.For a limited time, enjoy 30% off all eBooks at Packtpub.com. These books are more than just guides, they’re packed with real-world expertise from those who know the industry inside and out, offering perspectives you simply won’t find anywhere else.➽ Building AI Intensive Python ApplicationsThis book guides you through building powerful AI applications using large language models (LLMs), vector databases, and Python frameworks. You'll learn how to optimize AI performance, implement advanced techniques like retrieval-augmented generation, and tackle challenges like hallucinations and data leakage, ultimately creating reliable, high-impact AI solutions.Order Today at $41.98 $59.99➽ Databricks ML in ActionThis book is all about mastering the Databricks platform for machine learning and data science. It helps data engineers and scientists solve key problems by offering practical, cloud-agnostic examples and code projects. You’ll learn how to use Databricks tools to streamline workflows, improve model performance, and integrate with third-party apps.Order Today at $24.99 $35.99➽ Generative AI Application Integration PatternsThis book guides you through designing and integrating GenAI applications. You’ll learn essential tools and strategies, from prompt engineering to advanced techniques like retrieval-augmented generation. It provides practical examples, a clear 4-step framework, and covers ethical considerations for deploying GenAI models effectively.Order Today at $27.98 $39.99➽ Polars CookbookThis cookbook is your go-to guide for mastering Python Polars, a high-performance library for efficient data analysis. It offers step-by-step recipes for handling large datasets, advanced querying, and performance optimization. With practical tips on data manipulation, integration, and deployment, you'll boost your data workflows and analysis skills.Order Today at $24.99 $35.99➽ Building LLM Powered ApplicationsThis book helps you integrate LLMs into real-world apps using LangChain for orchestration. It covers the basics and advanced techniques of prompt engineering, explores various LLM architectures, and guides you through using powerful tools to create intelligent agents. You'll also learn about ethical considerations and the future of large foundation models.Order Today at $27.98 $39.99➽ Building Data-Driven Applications with LlamaIndexThis guide explores Generative AI and LlamaIndex, focusing on overcoming LLM limitations and building interactive applications. Learn to manage text chunking, security, and real-time data challenges. With hands-on projects, you'll master data ingestion, indexing, querying, and deployment, equipping you to develop and customize sophisticated AI-driven solutions.Order Today at $24.99 $35.99➽ Data Quality in the Age of AIThis book emphasizes the crucial role of data quality in AI success. It provides strategies to improve and measure data quality, offering practical steps to enhance data-driven decision-making. With real-world examples and actionable insights, it equips teams to optimize their data culture, leading to better AI performance and business outcomes.Order Today at $55.98 $79.99➽ Modern Computer Vision with PyTorch - Second EditionThis book offers a deep dive into neural network architectures and PyTorch for computer vision tasks. Learn to build solutions for image classification, object detection, and more using state-of-the-art models like CLIP and Stable Diffusion. With code available on GitHub and Google Colab, you'll gain practical skills for real-world applications and production deployment.Order Today at $33.99 $48.99➽ Accelerate Model Training with PyTorch 2.XThis book helps you optimize PyTorch model training, focusing on reducing build time and improving efficiency. Learn to speed up training with multicore systems, multi-GPU setups, and mixed precision. You'll explore techniques for model simplification, specialized libraries, and data pipeline improvements to enhance performance and model quality.Order Today at $24.99 $35.99➽ Mastering PyTorch - Second Edition This book guides you through building advanced neural network models with PyTorch, including CNNs, RNNs, and transformers. Learn to optimize training with GPUs, deploy models on mobile, and utilize libraries like Hugging Face and PyTorch Lightning. It covers deep learning across text, vision, and music, enhancing your AI skills with practical techniques.Order Today at $28.99 $41.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Apple Researchers Propose a Novel AI Algorithm to Optimize a Byte-Level Representation for Automatic Speech Recognition ASR and Compare it with UTF-8 Representation: The blog discusses a new method for enhancing multilingual automatic speech recognition (ASR) using vector quantized auto-encoders. This approach improves byte-level representation accuracy, optimizes resource usage, and reduces error rates, outperforming UTF-8 and character-based methods in multilingual settings.➽ PowerLM-3B and PowerMoE-3B Released by IBM: Revolutionizing Language Models with 3 Billion Parameters and Advanced Power Scheduler for Efficient Large-Scale AI Training. IBM's PowerLM-3B and PowerMoE-3B models showcase advancements in large-scale language model training. Utilizing IBM’s Power scheduler, these models achieve high efficiency and scalability, optimizing learning rates and computational costs for improved performance in NLP tasks.➽ AtScale Open-Sourced Semantic Modeling Language (SML): Transforming Analytics with Industry-Standard Framework for Interoperability, Reusability, and Multidimensional Data Modeling Across Platforms: AtScale has open-sourced its Semantic Modeling Language (SML) to create a standardized, interoperable language for semantic modeling across platforms. Built on YAML, SML supports complex data structures, promotes reusability, and integrates with modern development practices, aiming to enhance collaboration and efficiency in analytics.➽ LG AI Research Open-Sources EXAONEPath: Transforming Histopathology Image Analysis with a 285M Patch-level Pre-Trained Model for Variety of Medical Prediction, Reducing Genetic Testing Time and Costs: LG AI Research's EXAONEPath enhances digital histopathology by addressing Whole Slide Image (WSI) challenges with advanced self-supervised learning and stain normalization. This open-source model improves diagnostic accuracy, reduces genetic testing time, and supports various medical tasks.🚀 Trendspotting: What's Next in Tech Trends➽ How to Trace Memory Allocation in Python? This tutorial demonstrates how to use Python's `tracemalloc` module for tracing memory allocation in memory-intensive operations. It covers setting up a sample dataset, tracking memory usage before and after processing, and comparing snapshots to debug memory issues.➽ Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink: This post describes building a real-time anomaly detection system for time series data using AWS services. It outlines how to deploy an end-to-end solution with Amazon Managed Service for Apache Flink, Kafka, and SageMaker, focusing on detecting unusual patterns in streaming data.🛠️ Platform Showdown: Comparing ML Tools & Services➽ 7 Free Cloud IDE for Data Science That You Are Missing Out: To start data science projects quickly, explore these 7 Cloud IDEs: Kaggle Notebooks, Deepnote, Lightning.ai, Datalab by DataCamp, Google Colab, Amazon SageMaker Studio Lab, and DataLore. Each provides pre-built environments and free access to GPUs.➽ Developing End-to-End Data Science Pipelines with Data Ingestion, Processing, and Visualization: The article discusses the iterative nature of data science projects, emphasizing the importance of data ingestion, processing, and visualization. It outlines an end-to-end process involving business understanding, data preparation, model building, and monitoring.➽ Optimizing MLOps for Sustainability: The post outlines optimizing MLOps for sustainability using AWS by improving data preparation, model training, and deployment. Key practices include selecting low-carbon impact regions, using efficient storage, leveraging SageMaker’s tools, and monitoring with AWS services to minimize resource use and emissions.📊 Success Stories: Real-World ML Case Studies➽ GraphRAG auto-tuning provides rapid adaptation to new domains: Microsoft Research's GraphRAG uses large language models to build domain-specific knowledge graphs from text, enabling complex query responses. The tool automates the creation of domain-specific prompts to enhance graph accuracy and streamline knowledge extraction.➽ The “Who Does What” Guide to Enterprise Data Quality: This analysis explores enterprise data quality management, focusing on roles and processes in data detection, triage, resolution, and measurement. It highlights the importance of foundational versus derived data products, and strategies for improving data quality and efficiency.➽ Can AI Agents Do Your Day-to-Day Tasks on Apps? The blog introduces AppWorld, a new benchmarking framework for AI agents that interact with various apps to perform complex tasks. It features a simulated environment, a benchmark of intricate tasks, and a robust evaluation framework to test and improve AI agents’ performance.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ Google’s AI detective: The Needle in a Haystack test and how Gemini 1.5 Pro solves it. The blog discusses Google's Gemini 1.5 Pro, an AI model excelling in the "Needle in a Haystack" test. It showcases the model's ability to retrieve specific information from vast datasets across text, video, and audio, outperforming GPT-4 in complex retrieval tasks.➽ Regnology Automates Ticket-to-Code with GenAI on Vertex AI: The blog discusses Regnology's solution to the "Ticket-to-Code Problem," where bug reports are transformed into actionable code. Their Ticket-to-Code Writer tool, enhanced by Google’s Vertex AI and Gemini 1.5 Pro, automates this process, boosting efficiency by 60% and improving accuracy.➽ MedFuzz: Exploring the robustness of LLMs on medical challenge problems. LLMs excel in medical benchmarks but often oversimplify complex real-world scenarios. MedFuzz, inspired by security red-teaming and fuzzing, introduces adversarial challenges to test LLMs against these simplifying assumptions. This approach assesses their true effectiveness in nuanced clinical settings.*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

For a limited time, save on the best-selling books that will elevate your skills and knowledge! @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }👋 Hello ,✨ Welcome to Packt’s Signature Series: New Titles Just Arrived!📚 We’re excited to present a new collection in our Signature Series, featuring the best-selling titles in the data industry. Packed with insights on Generative AI and multimodal systems, this collection is available for a limited time at 30% off both print and e-book formats. This offer ends Sunday, September 22nd. Don’t miss your chance to upskill and elevate your career. Let’s dive in!➽ Building LLM Powered Applications: This new titleis all about helping engineers and data pros use large language models (LLMs) effectively. It tackles key challenges like embedding LLMs into real-world apps and mastering prompt engineering techniques. You’ll learn to orchestrate LLMs with LangChain and explore various models, making it easier to create intelligent systems that can handle both structured and unstructured data. It’s a great way to boost your skills, whether you’re new to AI or already experienced! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $34.98 $49.99➽ Python for Algorithmic Trading Cookbook: This bookis your go-to guide for using Python in trading. It helps you tackle key issues like acquiring and visualizing market data, designing and backtesting trading strategies, and deploying them live with APIs. You’ll learn practical techniques to gather data, analyze it, and optimize your strategies using tools like OpenBB and VectorBT. Whether you’re just starting or looking to refine your skills, this book equips you with the know-how to trade smarter with Python! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $36.99 $49.99➽ Microsoft Power BI Cookbook - Third Edition: The Power BI Cookbook is your essential guide to mastering data analysis and visualization with Power BI. It covers using Microsoft Data Fabric, managing Hybrid tables, and creating effective scorecards. Learn to transform complex data into clear visuals, implement robust models, and enhance reports with real-time data. This updated edition prepares you for future AI innovations, making it a must-have for beginners and seasoned users alike! Start your free trial for access, renewing at $19.99/month.eBook $29.99 $43.99Print + eBook $41.98 $59.99➽ The Definitive Guide to Power Query (M): The Definitive Guide to Power Query (M) focuses on mastering data transformation with Power Query. It covers fundamental and advanced concepts through hands-on examples that address real-world problems. You'll learn the Power Query M language, optimize performance, handle errors, and implement efficient data processes. By the end, you'll have the skills to enhance your data analysis effectively! Start your free trial for access, renewing at $19.99/month.eBook $43.99Print + eBook $37.99 $54.99➽ Mastering PyTorch - Second Edition: This is your essential resource for building advanced neural network models with PyTorch. You'll explore tools like Hugging Face, fastai, and Docker, learning to create models for text, images, and music. With hands-on examples, you'll master training optimization, mobile deployment, and various network types, equipping you to tackle complex AI tasks using the PyTorch ecosystem! Start your free trial for access, renewing at $19.99/month.eBook $28.99 $41.99Print + eBook $40.99 $51.99➽ Unlocking the Secrets of Prompt Engineering: It'syour guide to mastering AI-driven writing with large language models (LLMs). It covers essential techniques and applications, from content creation to chatbots. With practical examples, you'll learn to generate product descriptions and tackle advanced uses like podcast creation. The book emphasizes ethical practices and optimization strategies, preparing you to leverage AI for improved writing, creativity, and productivity! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99➽ ChatGPT for Cybersecurity Cookbook: Your essential guide to using AI in cybersecurity. It helps you automate tasks like penetration testing, risk assessment, and threat detection with ChatGPT. Each recipe provides step-by-step instructions for generating commands, writing code, and creating tools with the OpenAI API and Python. You'll explore innovative strategies and optimize workflows, gaining confidence in AI-driven techniques to excel in the rapidly evolving cybersecurity landscape! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $34.98 $49.99➽ Mastering NLP from Foundations to LLMs:Your complete guide to Natural Language Processing (NLP) with Python. It covers the mathematical foundations of machine learning and essential topics like linear algebra and statistics. You'll learn to preprocess text, classify it, and implement advanced techniques, including large language models (LLMs). With practical Python code samples and insights into future trends, you'll gain the skills to tackle real-world NLP challenges confidently and effectively design ML-NLP systems! Start your free trial for access, renewing at $19.99/month.eBook $29.99 $42.99Print + eBook $46.99 $52.99➽ Learn Microsoft Fabric: This title is your essential guide to using Microsoft Fabric for data integration and analytics. It explores key features with real-world examples, helping you build solutions for lakehouses, data warehouses, and real-time analytics. You'll learn to effectively monitor your Fabric platform and cover workloads like Data Factory and Power BI. By the end, you'll be equipped to unlock AI-driven insights and navigate the analytics landscape confidently! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $35.98 $44.99➽ Building Data-Driven Applications with LlamaIndex: This book is your comprehensive guide to leveraging Generative AI and large language models (LLMs). It addresses challenges like memory constraints and data gaps while teaching you to build interactive applications with LlamaIndex. You'll learn to ingest and index data, create optimized indexes, and query your knowledge base through hands-on projects. By the end, you'll be equipped to troubleshoot LLM issues and confidently deploy your AI-driven applications! Start your free trial for access, renewing at $19.99/month.eBook $24.99 $35.99Print + eBook $30.99 $44.99➽ OpenAI API Cookbook: This new title is all about using the OpenAI API to create smart applications. It helps engineers and data pros understand the basics, set up their API, and build tailored tools like chatbots and virtual assistants. You’ll learn practical recipes to enhance user experience and integrate AI into your workflows, making your projects more efficient and innovative! Start your free trial for access, renewing at $19.99/month.eBook $21.99 $31.99Print + eBook $27.98 $39.99Loved Those Titles? Check These Out!➽ Data Governance Handbook➽ Generative AI for Cloud Solutions➽ Data-Centric Machine Learning with Python➽ Modern Python Cookbook - Third EditionWe’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

BigQuery’s Contribution Model, Apache Airflow ETL on Google Cloud, Graviton4 EC2 Instances @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }Join Roman Lavrik from Deloitte Snyk hosted DevSecCon 2024Snyk is thrilled to announce DevSecCon 2024, Developing AI Trust Oct 8-9, a FREE virtual summit designed for DevOps, developer and security pros of all levels. Join Roman Lavrik from Deloitte, among many others, and learn some presciptive DevSecOps methods for AI-powered development.Save your spotSponsoredWelcome to DataPro #112—Your Weekly Fix of Data Science & ML Magic! 🌟In the fast-moving world of AI and ML, staying ahead means leveraging smart strategies for bold decisions. This week, we’re bringing you expert insights from our new Packt Signature Series. From real-time data mastery to AI modeling techniques, we’ve got everything you need to level up your data game!Get ready to elevate your model accuracy, supercharge performance, and cut costs with the latest in scalable solutions. Dive into this week’s must-read articles, tips, and practical techniques.📚 Must-Reads for Data Pros✦ LLM-Powered Apps: Build smarter AI tools✦ Python for Trading: Algorithmic insights✦ Power BI Cookbook: Master data visualization✦ The Prompt Engineering Playbook: Unlock AI secrets✦ Mastering PyTorch: Deep learning unleashed🔍 Algorithm Spotlight: Dive Deep into the Tech✦ Automating Metrics with Amazon Prometheus: Simplify data tracking on EKS✦ Graviton4 EC2 Instances: Memory-optimized power for your AI workloads✦ OpenAI Safety Practices: An update on securing AI✦ Mistral AI Release: Open-source models with unmatched flexibility🚀 Trendspotting: The Future of AI✦ Eureka AI Progress: Understand and evaluate AI advancements✦ OpenAI o1 System Card: A glance into AI innovations✦ Conversational Analytics Preview: What’s new in Looker?✦ Comet’s Opik: Streamlining LLM evaluation and prompt tracking🛠️ Tool Showdown: Which ML Platform Reigns Supreme?✦ BigQuery’s Contribution Model: Fresh insights for your data✦ Running Airflow on Google Cloud: Three easy approaches✦ Python Tricks: Merge dictionaries like a pro✦ Google AI’s DataGemma: A Set of Open Models that Utilize Data Commons📊 Case Studies: ML Success Stories✦ Handling Large Text with Longformer: A Hugging Face deep dive✦ Confluent & Vertex AI: Integrating LLMs for big wins✦ What Makes a Data Business Thrive? Lessons from the top🌍 ML Buzz: Industry News & Discoveries✦ Cracking PyTorch’s Mixed Precision Library: What you need to know✦ MLflow, Azure, Docker: Managing models with ease✦ Self-Learning Models: Teaching AI to improve autonomouslyGet ready for a week of data-driven breakthroughs!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 🌟💬Cheers,Merlyn Shelley,Editor-in-Chief, Packt.Sponsored📚 Packt Signature Series: Must-Reads & Author InsightsWe’re excited to present a new collection in our Signature Series, featuring the best-selling titles in the data industry. Packed with insights on Generative AI and multimodal systems, this collection is available for a limited time at 30% off both print and e-book formats. This offer ends Sunday, September 22nd. Don’t miss your chance to upskill and elevate your career. Let’s dive in!➽ Building LLM Powered Applications: This new titleis all about helping engineers and data pros use large language models (LLMs) effectively. It tackles key challenges like embedding LLMs into real-world apps and mastering prompt engineering techniques. You’ll learn to orchestrate LLMs with LangChain and explore various models, making it easier to create intelligent systems that can handle both structured and unstructured data. It’s a great way to boost your skills, whether you’re new to AI or already experienced! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $34.98 $49.99➽ Python for Algorithmic Trading Cookbook: This bookis your go-to guide for using Python in trading. It helps you tackle key issues like acquiring and visualizing market data, designing and backtesting trading strategies, and deploying them live with APIs. You’ll learn practical techniques to gather data, analyze it, and optimize your strategies using tools like OpenBB and VectorBT. Whether you’re just starting or looking to refine your skills, this book equips you with the know-how to trade smarter with Python! Start your free trial for access, renewing at $19.99/month.eBook $27.98 $39.99Print + eBook $36.99 $49.99➽ Microsoft Power BI Cookbook - Third Edition: The Power BI Cookbook is your essential guide to mastering data analysis and visualization with Power BI. It covers using Microsoft Data Fabric, managing Hybrid tables, and creating effective scorecards. Learn to transform complex data into clear visuals, implement robust models, and enhance reports with real-time data. This updated edition prepares you for future AI innovations, making it a must-have for beginners and seasoned users alike! Start your free trial for access, renewing at $19.99/month.eBook $29.99 $43.99Print + eBook $41.98 $59.99➽ The Definitive Guide to Power Query (M): The Definitive Guide to Power Query (M) focuses on mastering data transformation with Power Query. It covers fundamental and advanced concepts through hands-on examples that address real-world problems. You'll learn the Power Query M language, optimize performance, handle errors, and implement efficient data processes. By the end, you'll have the skills to enhance your data analysis effectively! Start your free trial for access, renewing at $19.99/month.eBook $43.99Print + eBook $37.99 $54.99🔍 Model Breakdown: Unveiling the Algorithm of the Week➽ Automating metrics collection on Amazon EKS with Amazon Managed Service for Prometheus managed scrapers: This blog discusses how Amazon Managed Service for Prometheus simplifies monitoring containerized applications in Amazon EKS by introducing a fully-managed, agentless scraper for Prometheus metrics, reducing operational overhead and enhancing efficiency through Terraform and AWS CloudFormation automation.➽ Now available: Graviton4-powered memory-optimized Amazon EC2 X8g instances. This post introduces Graviton-4-powered X8g instances, offering high memory, enhanced performance, scalability, and security for applications like databases and electronic design automation, emphasizing their efficiency, flexibility, and improved price-performance over previous instances.➽ An update on OpenAI safety & security practices: This post introduces OpenAI's Safety and Security Committee, outlining five key recommendations to enhance governance, security, transparency, collaboration, and safety frameworks for AI model development and deployment, ensuring responsible and secure advancements in AI technology.➽ Mistral AI Released Mistral-Small-Instruct-2409: A Game-Changing Open-Source Language Model Empowering Versatile AI Applications with Unmatched Efficiency and Accessibility. This article introduces Mistral AI's release of Mistral-Small-Instruct-2409, a powerful open-source large language model designed to enhance AI performance, promote accessibility, and support various natural language processing tasks with an emphasis on transparency, collaboration, and ethical AI development.🚀 Trendspotting: What's Next in Tech Trends➽ Eureka: Evaluating and understanding progress in AI. This post introduces the EUREKA framework for evaluating AI models, emphasizing the need for in-depth measurement beyond standard benchmarks. It aims to uncover strengths, weaknesses, and real-world capabilities of state-of-the-art models through transparent and reproducible evaluations.➽ OpenAI o1 System Card: This report outlines safety evaluations conducted before releasing OpenAI o1 models, addressing risks like bias, hallucinations, and disallowed content. It highlights mitigations, advanced reasoning capabilities, and overall safety ratings under OpenAI's Preparedness Framework.➽ Conversational Analytics in Looker is now in preview: This post introduces Looker's Conversational Analytics, powered by AI and Looker’s semantic model, enabling users to ask data questions in natural language. It simplifies business intelligence, enhances accessibility, and promotes data-driven decision-making across organizations.➽ Comet Launches Opik: A Comprehensive Open-Source Tool for End-to-End LLM Evaluation, Prompt Tracking, and Pre-Deployment Testing with Seamless Integration. This article introduces Opik, an open-source platform by Comet for enhancing observability and evaluation of large language models (LLMs). Opik helps developers and data scientists monitor, test, and track LLM applications, improving performance reliability and addressing issues like hallucinations.🛠️ Platform Showdown: Comparing ML Tools & Services➽ Introducing a new contribution analysis model in BigQuery: This post introduces contribution analysis in BigQuery ML, which helps organizations identify key data drivers behind trends and fluctuations, enabling faster, data-driven decisions by analyzing test and control datasets, and finding statistically significant contributors at scale.➽ Three different ways to run Apache Airflow ETL on Google Cloud: This article explores three ways to run Apache Airflow on Google Cloud, comparing Compute Engine, managed solutions, and infrastructure setups. It highlights the pros and cons of each, providing Terraform code for implementation.➽3 Simple Ways to Merge Python Dictionaries: This blog explains three common methods to merge dictionaries in Python: using the `update()` method, dictionary unpacking (`{**dict1, **dict2}`), and the union operator (`|`), providing code examples for each approach.➽ Google AI Introduces DataGemma: A Set of Open Models that Utilize Data Commons through Retrieval Interleaved Generation (RIG) and Retrieval Augmented Generation (RAG). Google's DataGemma addresses hallucinations in large language models (LLMs) by grounding them in real-world statistical data through Google’s Data Commons. It introduces two advanced models, RAG-27B-IT and RIG-27B-IT, enhancing precision for tasks requiring deep analysis and real-time fact-checking.📊 Success Stories: Real-World ML Case Studies➽ How to Handle Large Text Inputs with Longformer and Hugging Face Transformers? This post is a tutorial on using Longformer with Hugging Face Transformers for processing long text inputs in NLP tasks. It covers installing necessary packages, loading datasets, fine-tuning models, and evaluating results for tasks like review classification.➽ Integrating Confluent and Vertex AI with LLMs: This blog explains how integrating large language models (LLMs) with Confluent and Vertex AI automates SQL query generation, streamlining real-time data analytics. It enhances data exploration, report generation, pipeline optimization, and anomaly detection, addressing challenges like complex queries and real-time decision-making.➽ What Makes a Great Data Business? This post discusses how to identify and evaluate data businesses, highlighting their high margins and value potential. It covers key evaluation criteria: data sources, uses, nice-to-haves, and business models, providing a framework for private equity investors to spot valuable data businesses.🌍 ML Newsflash: Latest Industry Buzz & Discoveries➽ The Mystery Behind the PyTorch Automatic Mixed Precision Library: This article explains how to accelerate deep learning model training using Nvidia's automatic mixed precision (AMP) technique. It introduces Nvidia's Tensor cores, reviews the "Mixed Precision Training" paper, and demonstrates a 2X training speed-up for ResNet50 on FashionMNIST with minimal code changes.➽ Model Management with MLflow, Azure, and Docker: This article explains how to deploy MLflow, a tool for managing machine learning workflows, in a Docker container on Azure for scalability and collaboration. It covers MLflow's key components, focusing on MLflow Tracking, and provides a hands-on guide for setting up the system with Azure SQL Database and Blob Storage.➽ Teaching Your Model to Learn from Itself: This article explains pseudo-labeling, a semi-supervised learning technique that uses confident predictions from a model to label unlabeled data. A case study on the MNIST dataset demonstrates how pseudo-labeling boosted accuracy from 90% to 95% by iteratively adding confident predictions to the training set.We’ve got more great things coming your way—see you soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%;} #pad-desktop {display: none !important;} }

DeepSeek-AI’s Fire-Flyer AI-HPC, Microsoft’s Brain-Inspired AI Design, Fairness in Graph Filtering👋 Hello ,Happy Friday! 🌟Welcome to DataPro #109—Your Weekly Data Science & ML Digest! 🚀This week’s edition is packed with exciting updates! Discover Table-Augmented Generation (TAG) for smarter querying, Vectorlite v0.2.0 for speedy SQL-powered search, Zyphra's Zamba2-mini, and Weaviate's StructuredRAG for reliable AI outputs. Plus, we’ve curated top resources to supercharge your ML models with enhanced accuracy and efficiency!⚡ Tech Tidbits: Fresh Innovations and Tools▪️ AWS: Speed up AI inference with NVIDIA NIM on SageMaker and integrate Amazon Q with GitHub.▪️ Google ML: Explore multimodal search with BigQuery and get the lowdown on Imagen 3 on Vertex AI.▪️ Microsoft Research: Dive into brain-inspired AI design for next-gen tech.📚 Hot Reads from Packt Library▪️ Data Science Fundamentals Pocket Primer: Your essential guide to data science concepts.▪️ Mastering Looker and LookML: Create insightful views, dashboards, and databases.▪️ AI and Expert Systems: Techniques and applications for solving real-world problems.🔍 From Bits to BERT: LLMs & GPTs Spotlight▪️ TAG: Revolutionize database querying with a unified approach.▪️ Vectorlite v0.2.0: Get SQL-powered vector search with speed.▪️ StructuredRAG by Weaviate: Benchmark for reliable JSON outputs in AI.▪️ Cerebras DocChat: Fast, Llama 3-based GPT-4-level QA.▪️ Extension|OS: Open-source tool for on-demand AI access.▪️ AI21 Labs' Jamba 1.5: Quick, high-quality multilingual AI.▪️ LayerPano3D: AI framework for generating 3D scenes from text.▪️ Zyphra's Zamba2-mini: High-performance small language model.▪️ Fairness in Graph Filtering: Framework for better AI fairness.▪️ iAsk AI: Outperforming ChatGPT on MMLU Pro Test.▪️ DeepSeek-AI’s Fire-Flyer AI-HPC: Cost-effective deep learning solution.✨ On the Radar: What’s New & Noteworthy▪️ New LLM Agents: Exploring the latest architecture.▪️ Pandas Power: Advanced plotting techniques.▪️ AWS DeepRacer: Bridging the Sim2Real gap.▪️ MarianMT Translation: Easy language translation with Hugging Face Transformers.▪️ Building Transformers: A guide to training from scratch.▪️ ML Optimization: Top tips for boosting algorithm performance.Enjoy your weekend and stay ahead in the world of data science!DataPro Newsletter is not just a publication; it’s a complete toolkit for anyone serious about mastering the ever-changing landscape of data and AI. Grab your copyand start transforming your data expertise today!Calling Data & ML Enthusiasts!Want to share your insights and build your online reputation? Contribute to our new Packt DataPro column! Discuss tools, share experiences, or ask questions. Gain recognition among 128,000+ data professionals and boost your CV. Simply reply with your Google Docs link or use our feedback form. Whether you’re looking for visibility or a discreet approach, we’re here to support you.Share your content today and engage with our vibrant community! We’re excited to hear from you!Take our weekly survey and get a free PDF copy of our best-selling book,"Interactive Data Visualization with Python - Second Edition."We appreciate your input and hope you enjoy the book!Share Your Insights and Shine! 💬📚Expert Insights from Packt CommunityDid you know? “Books are the quietest, most constant friends, holding the world’s treasured wisdom. They offer gentle guidance and timeless lessons, passing their rich inheritance from one generation to the next.”We’re thrilled to bring you this week’s must-have new releases, straight from the experts to your bookshelf! Whether you're eager to enhance your skills or explore new horizons, now is the perfect moment to add these invaluable resources to your collection.For a limited time, enjoy 30% off all eBooks at Packtpub.com. These books are thoughtfully crafted by industry insiders with hands-on experience, offering unique insights you won’t find anywhere else.Don’t let these Packt-exclusive deals slip away—seize the opportunity to learn from the best at an unbeatable price!Order Today at $41.98 $59.99Data Science Fundamentals Pocket Primer: An Essential Guide to Data Science Concepts and TechniquesBy Mercury Learning and Information, Oswald CampesatoImagine having a go-to guide that gently walks you through the essentials of data science, making complex concepts feel accessible. This book does just that. With a blend of practical exercises and real-world examples, it simplifies the vast world of data science. Here’s what you’ll love:- A clear introduction to data science fundamentals.- Hands-on learning with practical examples.- Mastery of tools like Python, NumPy, Pandas, and R.- Techniques for data visualization to bring your data to life.Whether you're just starting or looking to sharpen your skills, this book is your companion on the journey to mastering data science.Get your copy now for $41.98 (originally $59.99).Order TodayMastering Looker and LookML - Complete Looker Guide for Developers: Master Looker and LookML to create views, dashboards, and databases with this guide [Video]By HHN Automate Book Inc.Embark on a journey to unlock the full potential of Looker with our all-encompassing course. Whether you’re new to Looker or looking to deepen your skills, this course guides you step-by-step through everything you need to know.Here’s what you can expect:- Hands-on tutorials for setting up your environment and connecting data.- In-depth exploration of LookML fields, parameters, and joins.- Advanced techniques for creating and managing impactful dashboards.By the end, you’ll have the confidence to create dynamic, data-driven insights that can drive meaningful decisions in your organization.Get the full video course now for $104.99 (MP4 download available).Order Today at $34.98 $49.99Artificial Intelligence and Expert Systems: Techniques and Applications for Problem SolvingBy Mercury Learning and Information ,I. Gupta ,G. NagpalDive into the world of AI with a guide that makes complex concepts approachable and practical. This book is your gateway to mastering AI, offering:- In-depth coverage of AI and expert systems.- Clear explanations paired with real-world applications.- Exploration of advanced topics like neural networks and fuzzy logic.From understanding the basics of AI to applying expert systems and neural networks, this book equips you with the tools to solve real-world problems. Perfect for anyone eager to enhance their knowledge of intelligent systems.Grab your copy now for $34.98 (originally $49.99).🔰 Data Science Tool Kit➤ NicolasHug/Surprise:Python scikit for building recommender systems with explicit rating data, emphasizing experiment control, dataset handling, and diverse prediction algorithms.➤ gorse-io/gorse:Open-source recommendation system in Go, designed for universal integration into online services, automating model training based on user interaction data.➤ recommenders-team/recommenders:Recommenders, a Linux Foundation project, offers Jupyter notebooks for building classic and cutting-edge recommendation systems, covering data prep, modeling, evaluation, optimization, and production deployment on Azure.➤ alibaba/Alink:Alink, developed by Alibaba's PAI team, integrates Flink for ML algorithms. PyAlink supports various Flink versions, maintaining compatibility up to Flink 1.13.➤ RUCAIBox/RecBole:RecBole, built on Python and PyTorch, facilitates research with 91 recommendation algorithms across general, sequential, context-aware, and knowledge-based categories.Access 100+ data tools in this specially curated blog, covering everything from data analytics to business intelligence—all in one place. Check out"Top 100+ Essential Data Science Tools & Repos: Streamline Your Workflow Today!"on PacktPub.com.⚡Tech Tidbits: Stay Wired to the Latest Industry Buzz!AWS ML Made Easy➤ Accelerate Generative AI Inference with NVIDIA NIM Microservices on Amazon SageMaker: The blog details NVIDIA's new NIM Inference Microservices integration with Amazon SageMaker, enabling fast, cost-effective deployment of large language models. It covers the use of prebuilt containers for efficient AI inferencing and provides a guide for setup and evaluation.➤ Connect the Amazon Q Business generative AI coding companion to your GitHub repositories with Amazon Q GitHub (Cloud) connector: This blog explains how incorporating generative AI, like Amazon Q Developer, can boost development productivity by up to 30% and streamline developer tasks. It details integrating Amazon Q Business with GitHub (Cloud) for natural language queries to manage repositories and enhance enterprise operations.Mastering ML with Google➤ Multimodel search using NLP, BigQuery and embeddings: This blog introduces a new era in search with multimodal embeddings, enabling text-based queries for images and videos. It showcases a demo for cross-modal search using Google Cloud Storage and BigQuery, allowing users to search for visual content through text queries.➤ A developer's guide to Imagen 3 on Vertex AI: The blog highlights user feedback on Imagen 3, emphasizing its need for high-quality, versatile image generation. It discusses improvements in artistic style, prompt adherence, and safety features like watermarking. Code examples illustrate creating photorealistic images and rendering text with the model.Microsoft Research Insights➤ Innovations in AI: Brain-inspired design for more capable and sustainable technology. Microsoft Research Asia, in collaboration with multiple institutions, is developing brain-inspired AI models to improve efficiency and sustainability. Key projects include CircuitNet for neural patterns, enhanced spiking neural networks (SNNs) for time-series prediction, and integrating central pattern generators for better sequence processing.🔍From Bits to BERT: Keeping Up with LLMs & GPTs➤ Table-Augmented Generation (TAG): A Unified Method for Improved Database Querying. Researchers from UC Berkeley and Stanford propose Table-Augmented Generation (TAG) to improve natural language queries over databases. TAG enhances query handling by combining query synthesis, execution, and answer generation, outperforming existing methods like Text2SQL and RAG in accuracy and complexity.➤ Vectorlite v0.2.0: Fast, SQL-Powered Vector Search with SQLite Driver. Vectorlite v0.2.0 enhances performance by using Google’s highway library for vector distance, addressing hnswlib’s limitations on SIMD instruction support and vector normalization. The update improves speed significantly, especially on x64 platforms with AVX2, and is now SIMD-accelerated on ARM.➤ StructuredRAG by Weaviate: Benchmark for Reliable JSON Output in AI. The StructuredRAG benchmark evaluates LLMs' ability to generate structured outputs like JSON. Testing Gemini 1.5 Pro and Llama 3 8B-instruct with various prompting strategies revealed an 82.55% success rate on average, with performance varying significantly by task and model.➤ Cerebras DocChat: Llama 3-Based GPT-4-Level QA in Hours. Cerebras has released two models for document-based Q&A: Llama3-DocChat and Dragon-DocChat, trained quickly using Cerebras Systems. Llama3-DocChat builds on Llama 3, while Dragon-DocChat improves on Dragon+ with enhanced recall. Both models and their training data are open-source.➤ Extension|OS: Open-Source Browser Tool for On-Demand AI Access. Extension|OS is a browser extension that integrates AI tools directly into web pages, allowing users to perform tasks like grammar checks and content edits without switching tabs. It features prompt customization, secure API key storage, and enhanced functionality with a Mixture of Agents.➤ AI21 Labs' Jamba 1.5 Models: Speedy, Quality, Multilingual AI. AI21's Jamba 1.5 Open Model Family features the Jamba 1.5 Mini and Large models, built on the SSM-Transformer architecture. They offer the longest context window, exceptional speed, and high quality. Jamba 1.5 models outperform competitors and support extensive enterprise applications.➤ LayerPano3D: AI Framework for Consistent 3D Scene Generation from Text. LayerPano3D introduces a novel framework for generating full-view, explorable panoramic 3D scenes from a single text prompt. By decomposing 2D panoramas into layered 3D representations, it achieves high-quality, consistent views and immersive exploration, surpassing existing methods.➤ Zyphra's Zamba2-mini: Efficient, High-Performance Small Language Model. Zamba2-1.2B improves hybrid SSM-transformer models by adding rotary embeddings and LoRA projectors for depth-specialization, enhancing performance. Developed to optimize model efficiency and accuracy, it’s applicable in real-world scenarios like advanced NLP tasks and code generation.➤ Fairness in Graph Filtering: Framework for Theory and Mitigation Techniques. The paper addresses fairness in GNN-based recommendation systems, which often overlook consumer fairness. It evaluates a new method for adjusting fairness via fair graph augmentation. This approach consistently improves fairness across various GNN models and datasets, advancing recommendation system equity.➤ iAsk Ai Outperforms ChatGPT and Others on MMLU Pro Test: The iAsk Pro model achieved a record 85.85% accuracy on the MMLU-Pro benchmark, surpassing all current LLMs, including GPT-4o, by over 13 percentage points. This dataset, with 12,000 complex questions, tests multi-task language comprehension rigorously. iAsk Pro's performance highlights its advanced reasoning and understanding capabilities, setting a new standard in AI evaluation.➤ Lite Oute 2 Mamba2Attn 250M: 10X More Efficient AI. The Lite Oute 2 Mamba2Attn 250M model, using the new Mamba2 architecture with attention layers, boasts 250 million parameters and achieves high benchmark scores. It was developed for improved efficiency and performance in various tasks, showing enhanced results in multiple evaluations compared to previous models.➤ DeepSeek-AI Launches Fire-Flyer AI-HPC: Cost-Effective Deep Learning Solution. The Fire-Flyer AI-HPC architecture addresses high costs and energy demands in Deep Learning by integrating hardware-software design. With 10,000 PCIe A100 GPUs, it cuts costs by 50% and reduces energy use by 40%, improving scalability and performance.✨On the Radar: Catch Up on What's Fresh➤ Navigating the New Types of LLM Agents and Architectures: The post explores the evolution of AI agents from early ReAct models to the second generation of more structured, efficient agents. It introduces tools and frameworks for building these agents and highlights advancements in design and performance. Key insights include improvements in routing and state management.➤ The Power of Pandas Plots: Backends. The article highlights how Pandas can leverage various visualization backends, such as Matplotlib, Plotly, and Hvplot, to enhance data visualization without extensive retraining. It shows how easy it is to switch between these backends for interactive and efficient plotting, emphasizing Hvplot's ease of use and integration.➤ AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap. The article focuses on training the AWS DeepRacer to safely navigate a track. It emphasizes creating a "safe" model that prioritizes staying on the track over speed. Key aspects include setting up the track, designing reward functions, and using a discrete action space. It details iterative training, starting with slower models and gradually increasing speed, to enhance both safety and performance. The final reward function balances staying on the track and adjusting speed for turns, with iterative improvements for increased reliability.➤ How to Translate Languages with MarianMT and Hugging Face Transformers? The article explains how to use MarianMT with Hugging Face Transformers for language translation. It covers installation, model selection, loading, tokenization, and translating text. The guide provides steps for translating to multiple languages and highlights MarianMT’s ease of use and effectiveness.➤ How to Build and Train a Transformer Model from Scratch with Hugging Face Transformers? The Hugging Face Transformers library enables both the use of pre-trained models and the creation of custom transformer models from scratch. This tutorial guides you through setting up, tokenizing data, configuring, and training a transformer for sentiment classification, emphasizing the need for high-performance computing resources.➤ 5 Tips for Optimizing Machine Learning Algorithms: This blog provides key tips for optimizing machine learning algorithms, focusing on data preparation, hyperparameter tuning, cross-validation, regularization, and ensemble methods. It aims to improve the accuracy, efficiency, and robustness of ML models for real-world applications.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{line-height:0;font-size:75%} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Free eBook: Debugging Apache Airflow® DAGsFree eBook: Fix your Airflow DAG errors fasterEven the most advanced Airflow users encounter DAG errors and task failures. That’s why we wrote Debugging Apache Airflow® DAGs. It’s a guide written by practitioners, for practitioners covering everything you need to know to solve issues with your DAGs:✅ Identifying issues during development✅ Using tools that make debugging more efficient✅ Conducting root cause analysis for complex pipelines in productionGET YOUR FREE GUIDE NOWSponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro 149- yourgo-to newsletter for all things Data and AI.This edition is packed with breakthroughs, experiments, and tutorials that show how fast the AI + data stack is evolving. From SQL-native memory engines to federated AI registries, adaptive defenses in federated learning, and even a 1950s algorithm powering computer vision, the highlights are designed to spark both curiosity and practical takeaways.Here’swhatyou’lldiscover 👇🔹MCP Registry Preview: DNS for AI Context-Meet the federated system for discovering AI servers, designed to scale like the internet itself.🔹Is Your Training Data Representative? PSI & Cramér’s V in Python- Learn how to measure representativeness, automate comparisons, and catch dataset drift before it breaks your models.🔹Fighting Back Against Attacks in Federated Learning-See how poisoning attacks work, why existing defenses fall short, and how adaptive strategies like EE-Trimmed Mean change the game.🔹Top 7 MCP Servers for Vibe Coding- From Git integration to browser automation and memory layers, these servers unlock context-rich collaboration between developers and AI agents.🔹NVIDIA’s Universal Deep Research (UDR)-A prototype framework that separates research strategy from the LLM itself, making deep research scalable, auditable, and customizable.🔹GibsonAIMemori: SQL-Native Memory for Agents-Forget costly vector DBs: this open-source memory engine makes agent memory transparent, portable, and cheap to run.Each story blendscutting-edgeideas with hands-on value,perfect for anyone building smarter AI systems, securing their pipelines, or just keeping ahead of the curve.So, without further ado, let’s jump in.Cheers,Merlyn ShelleyGrowth Lead, PacktTop Tools Driving New Research 🔧📊🔸MCP Team Launches the Preview Version of the 'MCP Registry': A Federated Discovery Layer for Enterprise AI.This blog unpacks the MCP Registry, a new open-source system designed as “DNS for AI context.” It explains why the federated model beats a single registry, how it secures enterprise AI, and what makes it scalable.You’llalso find details on its architecture, governance, and open-source foundation, plus practical FAQs for getting started with the preview release.🔸Building Advanced MCP (Model Context Protocol) Agents with Multi-Agent Coordination, Context Awareness, and Gemini Integration.Advanced MCP Agents can now be built and run insideJupyterorColabwith practical features like multi-agent coordination, context awareness, and Gemini integration. This tutorial shows how role-based agents such as researchers, analyzers, and executors work together as a swarm,maintainmemory for continuity, and deliver coherent results for complex, real-world AI tasks.🔸Is Your Training Data Representative? A Guide to Checking with PSI in Python:Checkingif your training data trulyrepresentsreality matters at build, deploy, andmonitorstages. This guide shows how to compare samples with PSI and Cramér’s V, from visual checks to robust stats, then automates the workflow in Python and exports an Excel report.You’llsee a worked example on Communities & Crime and clear thresholds for action.🔸Fighting Back Against Attacks in Federated Learning:Federated learning promises privacy-preserving training, but it also opens the door to subtle attacks like data poisoning and model manipulation. In this project, a multi-node simulator built onFEDnexplores how such attacks work, how currentdefenceshold up, and why adaptive strategies like EE-Trimmed Mean are needed. Experiments reveal lessons for making FL more resilient and trustworthy.Topics Catching Fire in Data Circles 🔥💬🔸Top 7 Model Context Protocol (MCP) Servers for Vibe Coding:Model Context Protocol servers areemergingas the backbone of Vibe Coding, where developers and AI agents collaborate in real time. This guide highlights seven standout MCP servers,from Git integration and live database access to browser automation, persistent memory, multi-agent orchestration, and research support,that make coding more adaptive, reproducible, and context-rich for modern development workflows.🔸How to Build a Complete End-to-End NLP Pipeline with Gensim: Topic Modeling, Word Embeddings, Semantic Search, and Advanced Text Analysis.An end-to-end NLP pipeline can be built inGensimthat covers preprocessing, topic modeling, embeddings, similarity search, and advanced analysis. This tutorial shows how to run it all inColab, from Word2Vec training and LDA topic modeling to coherence evaluation, visualization, and document classification. The result is a reusable framework for exploring and interpreting text data at scale.🔸Understanding the BigQuery column metadata (CMETA) index:BigQueryis pushing beyond petabyte-scale warehouses to petabyte-scale tables, where even metadata becomes big data. To keep queries fast and efficient, Google introduced the Column Metadata (CMETA) index, an automated, zero-maintenance system that prunes blocks early, saving time and slots. This blog explains how CMETA works, its impact on performance, and how to maximize its benefits.🔸When A Difference Actually Makes A Difference:A five-point gap on a bar chart can meanvery differentthings depending on variance, sample size, and effect size. In this bite-sized guide, Mena Wang shows business leaders how to look beyond averages, use statistical tests, and weigh effect sizes before acting. The lesson: not every “significant” difference is worth millions in investment.New Case Studies from the Tech Titans 🚀💡🔸NVIDIA AI Releases Universal Deep Research (UDR): A Prototype Framework for Scalable and Auditable Deep Research Agents.NVIDIA’s Universal Deep Research (UDR) is a prototype framework that separates research strategy from the underlying LLM, making deep research flexible, auditable, and scalable. Unlike rigid model-bound tools, UDR lets users design custom workflows, enforce validation rules, and swap models. With templates like Minimal, Expansive, and Intensive, UDR enables transparent, cost-efficient research pipelines for science, enterprise, and startups.🔸GKE Inference Gateway and Quickstart are GA:Google Cloud is expanding its AIHypercomputerstack with new inference capabilities in GKE Inference Gateway, now generally available. Highlights include prefix-aware routing for up to 96% faster TTFT, disaggregated serving for 60% higher throughput, and Anywhere Cache for 4.9x faster model loads. Paired with GKE InferenceQuickstart, teams can benchmark,optimize, and deploy LLM inference stacks in days instead of months.🔸Announcing Dataproc multi-tenant clusters:Google Cloud is introducingDataprocmulti-tenant clusters, giving data science teams a shared notebook environment that balances efficiency with strong isolation. Instead of siloed resources or weak security, admins can map users to service accounts, enforce IAM policies, and scalecomputedynamically. WithJupyterintegration via Vertex AI Workbench or third-party setups, teams get faster collaboration, lower costs, and enterprise-grade control.🔸Exploring Merit Order and Marginal Abatement Cost Curve in Python:This tutorial shows how to use Python to model electricity pricing anddecarbonisation. First, it builds a merit order curve to show how different power plants, ordered by cost, set the market price. Then it introduces a Marginal Abatement Cost Curve to comparedecarbonisationoptions by cost and impact. The code includes interactive charts to explore scenarios easily.Blog Pulse: What’s Moving Minds 🧠✨🔸GibsonAI Releases Memori: An Open-Source SQL-Native Memory Engine for AI Agents.GibsonAIhas releasedMemori, an open-source SQL-native memory engine for AI agents. Instead of relying on costly, opaque vector databases, Memori uses standard SQL (SQLite, PostgreSQL, MySQL) to provide persistent, transparent, and auditable memory. With a single line of code, agents gain context retention across sessions, reducing redundancy, cutting infrastructure costs by up to 90%, and giving users full control over their data.🔸Introducing Conversational Commerce agent on Vertex AI:Google Cloud has launched theConversational Commerce agent, now generally available in Vertex AI, to help retailers meet the shift toward longer, more complex search queries. Powered by Gemini, it enables natural, back-and-forth shopping conversations that guide users from discovery to checkout. Early adopters like Albertsons are seeing customers add more items to their carts, boosting sales through smarter, more intuitive product discovery.🔸Automate app deployment and security analysis with new Gemini CLI extensions:Google just introduced two newGemini CLIextensions that bring security and deployment right into your terminal. With/security:analyze, you can scan code for vulnerabilities locally (and soon in GitHub PRs) with clear, actionable fixes.With/deploy, you can ship apps directly toCloud Runin one simple command.It’sthe start of a broader, extensible Gemini CLI ecosystem.🔸The Hungarian Algorithm and Its Applications in ComputerVision:TheHungarian algorithm, first developed in the 1950s, is a powerful way to solve assignment problems, optimally matching tasks to workers, or objects across video frames. In computer vision, it underpinsmulti-object trackingby minimizing distances between bounding boxes detected in consecutive frames. This ensures consistent object tracking, even in complex scenes with motion, occlusion, or overlapping detections.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Join Airflow experts on Oct 22 for a live tour of 3.1’s biggest updates.Join Airflow experts on October 22 for a live walkthrough👋 Hello ,Your Airflow workflows just got smarter. Airflow 3.0 was the biggest release in the project’s history. Now, Airflow 3.1 introduces new features built for today’s challenges, from orchestrating GenAI pipelines that need human intervention to customizing your UI for non-technical stakeholders.Join Airflow experts on October 22 for a live walkthrough of all the updates you need to know:Human-in-the-loop enables mid-run workflow intervention including branch selection, task output approval/rejection, and text input for downstream tasksReact-based plugin interface extends the plugin system with support for React apps, external views, dashboard integrations, and menu integrationsUI improvements like favorited DAGs, internationalization, restored gantt charts, and performance insightsRegister NowWhether you’re already running 3.0 or waiting to see what's worth upgrading for, this webinar will get you up to speed on all of the latest changes and allow you to get your Airflow 3 questions answered live.Can’t join live? Register anyway and we’ll send you the recording.Sign Up for the RecordingSponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro Expert Insights #153!In this week’s Expert Edition, we’re excited to feature Eric Narro, Analytics Engineer and author of Getting Started with Taipy. Eric introduces a fresh perspective on how to move from time series to chatbots and bring your Python models to life with Taipy.For those new to it, Taipy is a Python application builder with one clear promise, to help you deploy data applications in real production environments. It’s the ideal tool for creating scalable, interactive apps that turn your models, analytics, and algorithms into powerful end-user experiences. Whether you’re building dashboards, optimization tools, or AI-powered chatbots, Taipy empowers data professionals to go from prototype to production with confidence.Let’s dive in!Cheers,Merlyn ShelleyGrowth Lead, PacktMeet Taipy: A Pure-Python, Fast, and Scalable Application BuilderBy Eric NarroFrom Time Series to Chatbots: Bring your Python Models to Life with TaipyTaipy is a Python application builder with one clear promise:deploy your data applications in real production environments. It’s the ideal tool for creating scalable, interactive apps that bring your models, analytics, and algorithms to life. Whether you’re building dashboards, optimization tools, or AI-powered chatbots, Taipy helps data professionals turn prototypes into powerful, end-user applications. WithGetting Started with Taipy, you’ll learn how to build complete applications from the ground up, deploy them confidently, and explore real-world examples and advanced use cases that showcase Taipy’s full potential.Python has long been the go-to language for data professionals, not because they’re developers, butbecause Python makes complex work accessible.Analysts, data scientists, and AI engineers use it to model data, run analytics, and visualize results.But when it comes to turning those models into real applications for end users, things get tricky. Building a web app the traditional way, with backend frameworks, databases, and front-end stacks, is often out of reach for data teams. It demands skills, time, and coordination that slow everything down and increase costs.Tools like Power BI or Tableau help visualize data, but they can’t trulyrunPython code or offer the flexibility of a full application. Python frameworks like Streamlit, Dash, Panel, or Gradio solve the problem partially. Each has trade-offs. To give an example, Streamlit is a great library for prototyping: it’s very easy to learn, and you can create demos in no time. While you can take Streamlit applications to production, they are harder to scale because they don’t optimize the way code runs, and they run on their own server (you can’t run them in a WSGI server). What this means is you can create useful applications for end users if they make limited use of the app, or if you don’t need to process large amounts of data.That’s where Taipy comes in!Taipy lets you create scalable, production-grade applications directly in Python.Whether for time series, optimization, geospatial analysis, or even LLM chatbots, Taipy is designed for performance and scalability.You can deploy Taipy apps on WSGI servers, handle multiple users efficiently, and still build everything using pure Python.Continue reading the full article on our Packt Medium Handle here.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Google’s Personal Health Agent, Bioinformatics AI Agent in Colab using Biopython [📽️ Webinar] Still guessing where to start with AI? We’ll show you. @media only screen and (max-width: 100%; width: 100%; padding-right: 20px !important } .hs-hm, table.hs-hm { display: none } .hs-hd { display: block !important } table.hs-hd { display: table !important } } @media only screen and (max-width: 100%; border-right: 1px solid #ccc !important; box-sizing: border-box } .hse-border-bottom-m { border-bottom: 1px solid #ccc !important } .hse-border-top-m { border-top: 1px solid #ccc !important } .hse-border-top-hm { border-top: none !important } .hse-border-bottom-hm { border-bottom: none !important } } .moz-text-html .hse-column-container { max-width: 100%; width: 100%; vertical-align: top } .moz-text-html .hse-section .hse-size-12 { max-width: 100%; width: 100%; width: 100%; vertical-align: top } .hse-section .hse-size-12 { max-width: 100%; width: 100%; padding-bottom: 0px !important } #section-0 .hse-column-container { background-color: #fff !important } } @media only screen and (max-width: 100%; padding-bottom: 0px !important } #section-2 .hse-column-container { background-color: #fff !important } } @media only screen and (max-width: 100%; padding-bottom: 0px !important } #section-3 .hse-column-container { background-color: #fff !important } } #hs_body #hs_cos_wrapper_main a[x-apple-data-detectors] { color: inherit !important; text-decoration: none !important; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit !important } a { text-decoration: underline } p { margin: 0 } body { -ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; -webkit-font-smoothing: antialiased; moz-osx-font-smoothing: grayscale } table { border-spacing: 0; mso-table-lspace: 0; mso-table-rspace: 0 } table, td { border-collapse: collapse } img { -ms-interpolation-mode: bicubic } p, a, li, td, blockquote { mso-line-height-rule: exactly } Don’t miss this exclusive presentation on September 30. View in browser Tuesday, September 30 | 11:00 AM ET / 8:00 AM PT Over the past several months, Camunda has worked with more than 50 customers to design and implement agentic orchestration solutions. This gave usa front-row view into how organizations are using AI agents to reshape operations: what works, what doesn’t, and what to do next. In this session, our team will share key takeaways from deployments across banking, insurance, healthcare, telecom, and other industries. We'll cover: Emerging patterns and proven best practices Common pitfalls to watch out for How AI agents integrate with human decision-making Measurable outcomes in speed, accuracy, and customer experience Whether you’re just starting your AI automation journey or scaling enterprise-wide, you’ll leave with practical guidance to make agentic orchestration work in your organization. Save Your Seat SponsoredSubscribe|Submit a tip|Advertise with UsYour Weekly Dose of Data & ML -Connecting Challenges to BreakthroughsWelcome toDataPro #148, your trusted guide through the fast-moving world of data science, machine learning, and AI infrastructure. Every week, we connect the toughest problems researchers and engineers face with the solutions shaping the next wave of innovation.This edition covers breakthroughs where AI directly tackles long-standing pain points:Faster Spark troubleshooting:Google’sGemini Cloud Assistpinpoints failures and bottlenecks in minutes, replacing hours of log-diving.Next-gen recommender systems:Yandex’sARGUSscales to a billion parameters, capturing long user histories and driving record engagement.Personalized health AI:Google’sPersonal Health Agentorchestrates multiple agents to deliveraccurate, trusted health guidance.Domain-specific LLMs:NetoAI’sTSLAM, trained on AWSTrainium, becomes the first open-source telecom LLM, cutting costs and boosting accuracy by 37%.Also inside: aColab-readyBioinformatics AI AgentwithBiopython,Baseten’s225% inference efficiency gains,FineVision’s24M multimodal dataset, andnew methods inDeepSpeed,LangExtract, Random Forest tuning, and Flink CMK encryption.AtDataPro, we believe keeping up with data and AIisn’tabout chasing hype,it’sabout understanding how problems get solved, and how those solutions expandwhat’spossible.Cheers,Merlyn ShelleyGrowth Lead, PacktTop Tools Driving New Research 🔧📊🔸Meet ARGUS: A Scalable AI Framework for Training Large Recommender Transformers to One Billion Parameters.Yandex introducedARGUS, a transformer-based recommender framework scaling to one billion parameters. It tackles long-standing issues of short memory, scalability, and adaptability by modeling extended user histories up to 8,192 interactions. Innovations include dual-objective pre-training, scalable encoders, and efficient fine-tuning. Deployed on Yandex Music, ARGUS achieved record gains: +2.26% listening time and +6.37% likes. This positions Yandex alongside Google, Netflix, and Meta as leaders in large-scale recommender systems.🔸Google AI Introduces Personal Health Agent (PHA): A Multi-Agent Framework that Enables Personalized Interactions to Address Individual Health Needs.Google introduced thePersonal Health Agent (PHA), a multi-agent framework built on Gemini 2.0 that integrates data science, domainexpertise, and health coaching via an orchestrator. Evaluated on 10 benchmarks with 7,000+ annotations and 1,100 expert hours, PHA outperformed baseline models in accuracy, personalization, and trust. Though still research, it sets a blueprint for modular, agentic health AI capable of reasoning across multimodal data.🔸How Baseten achieves 225% better cost-performance for AI inference:Baseten, in partnership with Google Cloud and NVIDIA, achieved225% better cost-performance for high-throughput AI inferenceand25% for latency-sensitive workloadsusing A4 VMs (NVIDIA Blackwell) and Google Cloud’s Dynamic Workload Scheduler. By combiningcutting-edgeGPUs,TensorRT-LLM, Dynamo, and multi-cloud redundancy,Basetendelivers scalable, resilient inference. This breakthrough lowers costs and unlocks real-time, production-ready AI applications across industries, from agentic workflows to media and healthcare.Topics Catching Fire in Data Circles 🔥💬🔸Implementing DeepSpeed for Scalable Transformers: Advanced Training with Gradient Checkpointing and Parallelism.This advancedDeepSpeedtutorialdemonstrateshow to efficiently train large transformers usingZeROoptimization, FP16 mixed precision, gradient accumulation, and advanced parallelism. It covers full workflows: model setup, dataset creation, GPU memory monitoring, checkpointing, inference, and benchmarkingZeROstages. Learners gain hands-on practice with gradient checkpointing, CPU offloading, and advanced features like pipeline andMoEparallelism, making large-scale LLM training accessible evenonresource-limited environments likeColab.🔸Troubleshoot Apache Spark on Dataproc with Gemini Cloud Assist AI:Google Cloud introducedGemini Cloud Assist InvestigationsforDataprocand Serverless for Apache Spark, an AI-powered tool that diagnoses job failures and performance bottlenecks. It analyzes logs, metrics, and configs across services to pinpoint root causes, whether infrastructure, configuration, application, or data issues, and provides actionable fixes. Accessible via console or API, it accelerates troubleshooting, boosts team efficiency, and empowers engineers without deep Sparkexpertiseto resolve issues quickly.🔸Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows:LangExtractis a workflow library forLLM-based structured extractionthat fixes schema drift and missing facts via prompt orchestration, chunking, and optional parallel or multi-pass extraction. It fine-tunes prompts per model, manages token limits, and streams results as generator outputs. A hands-on demo ingestsTechXploreRSS, filters articles, runs few-shot extractions (e.g., sectors, metrics, values, regions), and aggregates results intodataframes. Best practices: rich examples, 2+ extraction passes, and tunedmax_workers.🔸The Beauty of Space-Filling Curves: Understanding the Hilbert Curve.Hilbert curve, a classic space-filling curve, links 1D order to n-D coordinates while preserving locality, vital for big-data systems (e.g., Databricks liquid clustering) and ML on spatial data. The article surveys SFC history(Peano→Hilbert), properties (continuous, surjective,Hausdorffdim 2), and a practical implementation usingSkilling’s algorithm(binary→Graycode, bit disentanglement, XOR rotations) for fastindex↔coordinatemapping. Applications include partitioning, clustering, indexing, compression, and efficient range queries with fewer fragmented clusters.New Case Studies from the Tech Titans 🚀💡🔸How to Create a Bioinformatics AI Agent Using Biopython for DNA and Protein Analysis.Build aBioinformatics AI AgentinColabusingBiopythonto streamline DNA/protein analysis. The tutorial wraps sequence fetching (NCBI), composition/GC%/MW,translationand protein stats,MSA,phylogenetic trees,motif search,codon usage, andGC sliding windowsinto one class withPlotly/Matplotlibvisuals. Start with sample sequences (SARS-CoV-2 Spike, Human Insulin, E. coli 16S) or custom accessions.It’sa hands-on, end-to-end pipeline for education, research, and rapid prototyping.🔸How NetoAI trained a Telecom-specific large language model using Amazon SageMaker and AWS Trainium.NetoAIbuiltTSLAM, the first open-sourcetelecom-specific LLM, by fine-tuningLlama-3.1-8BwithLoRAonAWSTrainium(Trn1)viaAmazon SageMaker.Trainiumcut training time to <3 days and lowered costs, while SageMaker ensured scalability and compliance. Deployed onAWS Inferentia2, TSLAM delivers low-latency inference for real-world telco agents (fault diagnosis, customer service, planning, config management). Results:86.2% accuracy vs. 63.1% base, ~37% performance gain, with plans to scale further onTrn2.🔸Zero-Inflated Data: A Comparison of Regression Models:Zero-inflated data occurs when a dataset has far more zeros than expected, such as bike usage where most people report zero days. Standard Poisson regression struggles with this, so specialized models work better. TheZero-Inflated Poisson (ZIP)model handles excess zeros by combining a Bernoulli zero model with a Poisson count model, whilehurdle modelsfirst predict zero vs. non-zero and then model only the positives. In practice, both outperform Poisson or linear regression, with hurdle models offering a faster, solid fit and ZIP excelling when the data truly follows a zero-inflated pattern.Blog Pulse: What’s Moving Minds 🧠✨🔸Hugging Face Open-Sourced FineVision: A New Multimodal Dataset with24 Million Samples for Training Vision-Language Models (VLMs).Hugging Face releasedFineVision, a massive open multimodal dataset with17.3M images, 24.3M samples, and 10B tokens, built from 200+ sources and carefully cleaned, rated, and deduplicated. Covering domains from VQA and OCR to charts, science, and GUI navigation, it delivers up to46% performance gainsover prior datasets, with only1% benchmark leakage. Fully open-sourced,FineVisionsets a new standard for training robust, diverse, and reproducible vision-language models.🔸Achieve full control over your data encryption using customer managed keys in Amazon Managed Service for Apache Flink.Amazon Managed Service for Apache Flink now supportscustomer managed keys (CMKs)in AWS KMS, giving organizations full control over data encryption for checkpoints, snapshots, and running state. While the service already encrypts data by default with AWS-owned keys, CMKs let you manage lifecycle policies, enforce least-privilege access, and meet strict compliance requirements. Enabling CMKs involves defining IAM/operator policies, updating the application with the CMK, and restarting for changes to take effect. Supported fromFlink runtime 1.20, this feature balances strong security with operational flexibility.🔸A Visual Guide to Tuning Random Forest Hyperparameters:This post explores howhyperparameter tuning affects Random Forests, using the California housing dataset. A default forest (100 trees, unlimited depth) already outperforms tuned decision trees, highlighting the strength of ensembles. Visualizations of trees, predictions, errors, and feature importances show how forests reduce variance. Experiments with depth limits,n_estimators,n_jobs, and Bayes search reveal trade-offs: more trees or tuning slightly improve metrics (MAE ~0.31, R² ~0.83) butgreatly increasetraining time.Takeaway:Random forests offerstrong performanceout-of-the-box, but tuning brings marginal gains at significant computational cost.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}} @media only screen and (max-width: 100%; width: 100%; padding-right: 20px !important } .hs-hm, table.hs-hm { display: none } .hs-hd { display: block !important } table.hs-hd { display: table !important } } @media only screen and (max-width: 100%; border-right: 1px solid #ccc !important; box-sizing: border-box } .hse-border-bottom-m { border-bottom: 1px solid #ccc !important } .hse-border-top-m { border-top: 1px solid #ccc !important } .hse-border-top-hm { border-top: none !important } .hse-border-bottom-hm { border-bottom: none !important } } .moz-text-html .hse-column-container { max-width: 100%; width: 100%; vertical-align: top } .moz-text-html .hse-section .hse-size-12 { max-width: 100%; width: 100%; width: 100%; vertical-align: top } .hse-section .hse-size-12 { max-width: 100%; width: 100%; padding-bottom: 0px !important } #section-0 .hse-column-container { background-color: #fff !important } } @media only screen and (max-width: 100%; padding-bottom: 0px !important } #section-2 .hse-column-container { background-color: #fff !important } } @media only screen and (max-width: 100%; padding-bottom: 0px !important } #section-3 .hse-column-container { background-color: #fff !important } } #hs_body #hs_cos_wrapper_main a[x-apple-data-detectors] { color: inherit !important; text-decoration: none !important; font-size: inherit !important; font-family: inherit !important; font-weight: inherit !important; line-height: inherit !important } a { text-decoration: underline } p { margin: 0 } body { -ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; -webkit-font-smoothing: antialiased; moz-osx-font-smoothing: grayscale } table { border-spacing: 0; mso-table-lspace: 0; mso-table-rspace: 0 } table, td { border-collapse: collapse } img { -ms-interpolation-mode: bicubic } p, a, li, td, blockquote { mso-line-height-rule: exactly }

From Prompt to Purpose: Agentic AI and the Rise of Autonomous IntelligenceBecome the AI Generalist that makes big $ Using AIDid you know that, Sam Altman has predicted that by 2025, AI will impact over 50% of knowledge-based jobs, data analysis, financial planning, strategic decisions, auditing, and creative work that once required specialists.While others worry about being replaced, you can profit from this transformation. The future belongs to AI-powered generalists who can leverage AI to deliver specialist-level results.And you could be the next one to do it!So..Join Outskill's 2 day AI- Mastermind this weekend (usually for $895) and become an AI expert.Register now for freeWhen: Saturday and Sunday, 10 AM - 7 PM.In just 16 hours & 5 sessions, you will:✅ Build AI Agents and custom bots that handle your repetitive work and free up 20+ hours weekly✅ Learn how AI really works by learning 10+ AI tools, LLM models and their practical use cases.✅ Learn to build websites and ship products faster, in days instead of months✅ Create professional images and videos for your business, social media, and marketing campaigns.✅ Turn these AI skills into10$k income by consulting or starting your own AI services business.Learn million $ insights used by biggest giants like google, amazon, microsoft from their practitioners 🚀🔥Unlock bonuses worth $5100 in 2 days!🔒day 1:3000+ Prompt Bible🔒day 2: Roadmap to make $10K/month with AI🎁Additional bonus: Your Personal AI Toolkit BuilderJoin now for $0SponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro 147 – Expert-Led EditionYour Weekly Brief on What’s Next in AI, ML, and Data EngineeringThis week, we’re featuring an expert insight from Sagar Lad, Data & AI Solution Architect, who unpacks a pivotal evolution in artificial intelligence: the emergence of Agentic AI,intelligent systems that don’t just respond, but pursue goals, adapt in real time, and collaborate with other agents to get things done.For data scientists, ML engineers, and AI practitioners, Agentic AI marks a fundamental shift. Most of today’s AI systems are reactive, they answer prompts, complete predefined tasks, or generate outputs within limited contexts. Agentic systems are different. They perceive, reason, act, and learn, enabling multi-step autonomy in enterprise and real-world environments.In this technical deep dive, Sagar explores:🔹What Agentic AI is and why it matters for the next wave of AI systems🔹How modern architectures blend LLMs, memory, tool use, and orchestration🔹The enabling technologies: LangChain, Semantic Kernel, vector databases, cloud-native platforms, and more🔹Challenges like LLM brittleness, multi-agent coordination, and security risks🔹And how Agentic AI is already finding footholds in data engineering workflows, MLOps, and autonomous decision systemsIf you’re working at the edge of data and intelligence, this is the edition to bookmark.Let’s dive in 👇 For tech leaders shaping AI strategy in the enterpriseAI adoption brings real pressures:Prove ROI on LLM initiatives.Protect data privacy & compliance when using open-source models.Scale responsibly without being derailed by hallucinations, talent gaps, or security risks.That’s why we built TechLeader Voices by Packt — a newsletter that delivers real-world playbooks, frameworks, and lessons from frontline AI leaders.Subscribe and unlock the Executive Insights Pack — including 1 report, 1 case study, and 5 power talks — valid for the next 48 hours only.Join TechLeader Voices to Access the PackCheers,Merlyn ShelleyGrowth Lead, PacktAgentic AI: The Next Leap in Intelligent Systems | by Sagar LadArtificial Intelligence has already transformed industries with predictive analytics, natural language understanding, and generative capabilities. But most AI systems today are reactive — they respond to prompts, execute predefined tasks, or generate outputs within bounded contexts. The next evolution is Agentic AI: systems that can act autonomously, pursue goals, adapt to environments, and coordinate with other agents to achieve outcomes with minimal human intervention.This article explores what Agentic AI is, why it matters, its architectural principles, key enablers, technical challenges, and enterprise applications.What is Agentic AI?At its core, Agentic AIrepresentsa shift from stateless, prompt-driven systems (e.g., today’s chatbots and LLMs) to autonomous, goal-oriented agents. An agentic AI system can:Perceive— Gather information from structured and unstructured sources (APIs, sensors, documents).Reason— Apply contextual knowledge, logic, and planning todeterminethe best course of action.Act— Execute tasks, trigger workflows, or interact with digital/physical systems.Adapt— Learn from feedback, outcomes, and environment changes to improve future performance.Agentic AI at its CoreUnlike traditional automation or AI models that need constant supervision, agentic systems can plan, prioritize, and execute multi-step tasks independently.The convergence of several technological trends is accelerating the rise of Agentic AI:Large Language Models (LLMs) as Reasoning Engines: Modern LLMs can interpret vague instructions, break them into sub-tasks, and suggest solutions.Tool Augmentation: APIs and plugins extend AI capabilities beyond text generation into search, data retrieval, code execution, and robotic control.Memory Architectures: Vector databases and knowledge graphs allow agents to store, recall, and refine knowledge over time.Orchestration Frameworks: Platforms like LangChain, Semantic Kernel, and Microsoft Prompt Flow enable chaining of multiple reasoning steps and tool calls.Cloud-Native AI Platforms: Services like Azure AI Foundry and AWS Bedrock are simplifying deployment and scaling of multi-agent systems.This technological maturity makes it possible to design agents that can operate with goal-directed autonomy while still adhering to enterprise safety, governance, and compliance standards.Architectural Principles of Agentic AIAgentic AI solutions typically follow a layered architecture:Perception Layer: Responsible for gathering and interpreting data from the environment. Technologies include sensors, Natural Language Processing (NLP), and Computer Vision to perceive text, images, and speech.Cognitive Layer: The brain of the system, encompassing reasoning and decision-making. Employs machine learning models, including reinforcement learning, to analyze inputs and predict outcomes.Action Layer: Executes decisions through physical or digital means. Incorporates feedback loops for self-correction and continuous improvement.Communication Layer: Enables interaction with users and other systems. Supports multimodal communication (e.g., text, voice, visual) for seamless integration.This modular design ensures that agents are not “black boxes” but traceable, governed systems that can fit into enterprise architecture.Key Enablers1. Autonomous PlanningAgents can break down goals into sub-goals and dynamically re-plan when obstacles occur. For example, an AI project manager could reassign tasks if a resource becomes unavailable.2. Tool Use and API IntegrationBy connecting to enterprise systems (like SAP, Salesforce, or Azure DevOps), agents move fromknowledge workerstoexecution workers.3. Multi-Agent CollaborationInstead of a single agent, ecosystems of specialized agents can cooperate. Example: one agent handles data retrieval, another validates compliance, while a third presents the final report.4. Persistent MemoryUnlike stateless chatbots, agentic systems remember previous interactions, allowing continuity in long-term projects or customer engagements.5. Responsible AI ControlsAgentic AI cannot succeed withoutrobust guardrails: bias detection, safety filters, role-based access, and explainability features.Challenges in Building Agentic AIDespite the potential, several technical and organizational challenges must be addressed:Reliability of LLM Reasoning— Current models may hallucinate or produce brittle plans. Agents must include validation and error recovery.Scalability of Multi-Agent Systems— Coordinating multiple agents without excessive overhead is non-trivial.Integration Complexity— Enterprises run heterogeneous systems; seamless API orchestration is essential.Security Risks— Autonomous agents with execution powers increase risks of unauthorized actions, data leakage, or adversarial prompts.Ethical and Compliance Concerns— Decisions must align with legal and regulatory requirements, particularly in sensitive domains like healthcare and finance.Enterprise ApplicationsSoftware EngineeringAgents that debug code, run unit tests, and deploy fixes.Autonomous backlog grooming and sprint planning.Data & AnalyticsAutomated data quality checks, lineage tracing, and governance enforcement.Agents that query data warehouses, generate insights, and prepare visualizations.Customer ExperienceProactive agents that resolve issues without waiting for customer complaints.Multi-modal support agents integrating voice, chat, and visual instructions.Business OperationsIntelligent RPA 2.0: replacing static workflows with adaptive agents.Supply chain optimization: monitoring inventory, predicting delays, re-routing shipments.Knowledge ManagementContinuous synthesis of insights from documents, emails, and reports.Agents that maintain living enterprise knowledge bases.The Road AheadAgentic AI represents a paradigm shift: from “AI as a tool” to “AI as a collaborator.” The near future will likely see:Standardization of Agent Frameworks— Interoperability between different orchestration tools and vendors.Enterprise AI Operating Systems— Platforms that manage agent lifecycles, policies, and performance.Specialized Industry Agents— Domain-specific agents trained on healthcare protocols, financial compliance, or manufacturing processes.Human-Agent Collaboration Models— Workflows where humans define intent and agents execute while keeping humans in control of critical decisions.ConclusionAgentic AI has the potential to transform enterprises fromdata-driventogoal-drivenorganizations. By combining reasoning, memory, and autonomous action, agents can handle complex workflows that once required human supervision. Yet, this power must be matched with strong governance, safety, and ethical oversight.For technical leaders, the challenge is not justbuilding powerful agents, butbuilding trustworthy ones. The organizations that succeed will be those that strike the right balance between autonomy and accountability, unlocking productivity gains while maintaining control.The age of Agentic AI has begun — not as a replacement for human intelligence, but as a force multiplier that augments human capabilities and accelerates digital transformation.Dive deeper and read the full piece on PacktHub Medium.We’ll be back with more soon!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

FREE GUIDE: Airflow 3 Tips & Code SnippetsFREE GUIDE: Airflow 3 Tips & Code SnippetsThinking about upgrading to Apache Airflow® 3? You’ll get powerful new features like a modernized UI, event-based scheduling, and streamlined backfills. Quick Notes: Airflow 3 Tips & Code Snippets is a concise, code-filled guide to help you start developing DAGs in Airflow 3 today.You’ll learn:How to run Airflow 3 locally (with dark mode) and navigate the new UIHow to manage DAG versioning and write DAGs with the new @asset-oriented approachThe key architectural changes from Airflow 2 to 3GET YOUR FREE GUIDESponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro #146: Expert Insight Edition.We’re excited to bring on board Sagar Lad, Lead Data Solution Architect at a leading Dutch bank, to the Expert Insight edition of the DataPro newsletter. Sagar will be sharing his hard-won lessons, practical tips, and implementation strategies for navigating the challenges of data in the Gen AI and Agentic AI era.Each week, Sagar will guide you through his in-depth analysis and research, showing what really works in complex production environments. His goal is simple: help you turn concepts into practice and ideas into impact.This week, he kicks things off with a deep dive into Data Products: Turning Data into Tangible Value. As always, our mission at DataPro is to bring you first-hand, practical insights from industry experts. We believe Sagar’s expertise will provide valuable guidance you can apply directly to your daily data practice.So, without further ado, let’s jump in.Cheers,Merlyn ShelleyGrowth Lead, PacktData Products: Turning Data into Tangible Value - By Sagar LadIn today’s digital economy, data has become one of the most valuable assets for organizations. Every transaction, interaction, and process generates data that — when properly harnessed — can unlock powerful insights, drive innovation, and create competitive advantages. However, simply collecting and storing vast amounts of data is not enough. To truly realize its value, organizations must transform data into usable, scalable, and outcome-driven solutions. This is where the concept of adata productcomes into play.A data product is not just raw data, but rather a packaged, consumable, and value-generating asset built on top of data. Just as traditional products solve customer needs, data products solve business challenges by delivering insights, predictions, or automated decisions in a way that is accessible and reliable for end users.What is a Data Product?At its core, adata productis a solution designed around data to serve a specific purpose or generate business value. It could take many forms — such as a dashboard, an API serving machine learning predictions, a recommendation engine, or even a dataset curated for a particular domain.For example:→ Netflix’s recommendation systemis a data product built to enhance user engagement.Characteristics of a data product include:1. Purpose-driven— It is built to achieve a clear outcome (e.g., increase sales, reduce costs, improve customer satisfaction).2. Reusable— A well-designed data product can serve multiple teams or applications.3. Consumable— It is packaged in a way that non-technical users or systems can leverage it seamlessly.4. Scalable— It is designed to evolve with changing business needs and data volumes.Data Product: Bridge between Producer & ConsumerData Products vs. Data AssetsIt is important to differentiate betweendata assetsanddata products.Adata assetcould be a data lake, warehouse, or dataset that stores raw or processed data. While valuable, assets by themselves may not generate outcomes unless someone analyzes them.Adata product, on the other hand, transforms these assets into actionable, consumable outputs that stakeholders can directly use to make decisions or power business processes.In other words, data assets are ingredients, while data products are the finished dishes that customers can consume.Why Do Organizations Need Data Products?Organizations often struggle with extracting value from their data investments. Billions of dollars are spent globally on data platforms, yet many businesses face the“last mile problem”— where insights fail to reach decision-makers in a meaningful way. Data products help bridge this gap by operationalizing data and embedding it into workflows.Key benefits of data products include:1. Faster Decision-MakingWith well-packaged insights, business users don’t need to spend hours querying databases or waiting for reports. A data product like a sales forecasting model can instantly provide actionable intelligence.2. Democratization of DataData products abstract technical complexity, enabling business users, analysts, and applications to easily consume data-driven insights.3. Standardization and ReusabilityInstead of rebuilding analytics pipelines repeatedly, a single data product can serve multiple business units. For example, a customer segmentation data product could be reused by marketing, sales, and product teams.4. Scalability and AutomationData products, once designed, can be scaled to handle growing data volumes and embedded into automated workflows.5. Value RealizationUltimately, data products help organizations move beyond storing data tomonetizing and operationalizing it— whether through cost savings, revenue generation, or improved customer experiences.Key Principles for Designing Data ProductsDesigning a successful data product requires more than technical skills — it requires product thinking. Some guiding principles include:1.Start with Business ValueA data product must solve a real business problem. Before building, clearly define the outcome it should drive.2. User-Centric DesignThe product should be intuitive for its target users, whether that’s executives, developers, or customers.3. Trust & TransparencyUsers must trust the data product. This requires data quality checks, explainability in AI models, and governance measures.4. Scalability & ReusabilityBuild products that can adapt to future needs, serve multiple stakeholders, and scale across datasets and domains.5. OperationalizationA data product should integrate seamlessly into business workflows and systems, rather than existing as a standalone artifact.6. Monitoring & ImprovementData products must be continuously monitored for performance, accuracy, and relevance, with feedback loops for improvements.Challenges in Building Data ProductsWhile data products are powerful, organizations face challenges in creating and scaling them:1. Data Quality Issues: Poor data leads to unreliable products.2. Cultural Resistance: Teams may hesitate to trust automated insights.3. Lack of Product Mindset: Many companies treat data as IT projects, not products.4. Scalability Hurdles: A data product may work for a pilot but struggle in enterprise-wide deployments.5. Governance & Compliance: Ensuring data products adhere to regulatory and ethical standards is critical.Overcoming these requires strongdata governance, clear ownership, cross-functional collaboration, and a product-centric approach.The Role of Data Mesh and Data ProductsThe concept ofdata productsis also central toData Mesharchitecture. In Data Mesh, each domain team is responsible for building and managing its own data products, treating them as first-class citizens. This shifts ownership from centralized IT teams to domain experts, making data products more relevant, accurate, and consumable.By combining Data Mesh principles with robust product management practices, organizations can scale their data strategy while ensuring alignment with business outcomes.Future of Data ProductsThe future of data products looks promising as technology evolves:1. AI-driven Data Products: With advancements in generative AI, data products will become more conversational, adaptive, and personalized.2. Marketplace of Data Products: Organizations may buy and sell data products just like SaaS solutions, creating new revenue streams.3. Self-Service Ecosystems: Business users will increasingly be able to design their own data products using no-code/low-code platforms.4. Embedded Trust & Ethics: As AI governance matures, responsible AI principles will be embedded directly into data products.ConclusionData products represent a fundamental shift in how organizations leverage data. They move beyond static reports or siloed datasets to create reusable, scalable, and outcome-driven solutions. By applying product thinking to data initiatives, companies can ensure that data investments directly translate into measurable business value.In a world where data is the new currency,data products are the vehicles that convert raw information into tangible value. The organizations that master this art will be the ones that thrive in the data-driven future.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Gemma 3 270M, Model Predictive Control (MPC) Using Python and CasADiThe 16 hour AI challenge: Learn AI, build Products & Earn $10K/MonthChatGPT 5 just dropped and guess what? 300 million jobs became obsolete overnight.While companies are panic-firing entire departments, a small group of AI-skilled professionals are charging $10K/month as consultants to automate those same jobs.The difference? They know the frameworks, workflows, and monetization strategies that 99% of people don't.Join Outskill's 16-Hour AI Sprint this weekend (usually for $895) and become the AI expert companies are desperately hiring – not firing. Register now for freeDate: Saturday and Sunday, 10 AM - 7 PM. Rated 9.8/10 by trustpilot– an opportunity that makes you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours & 5 sessions, you will:✅ Build AI Agents and custom bots that handle your repetitive work and free up 20+ hours weekly✅ Learn how AI really works by learning 10+ AI tools, LLM models and their practical use cases.✅ Learn to build websites and ship products faster, in days instead of months✅ Create professional images and videos for your business, social media, and marketing campaigns.✅ Turn these AI skills into10$k income by consulting or starting your own AI services business.Learn million $ insights used by biggest giants like google, amazon, microsoft from their practitioners 🚀🔥Unlock bonuses worth $5100 in 2 days!🔒day 1:3000+ Prompt Bible🔒day 2: Roadmap to make $10K/month with AI🎁Additional bonus: Your Personal AI Toolkit BuilderJoin now for $0SponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro 145 ~your go-to guide for all things data and AI.You might be wondering why DataPro landed in your inbox on a Monday. We’re experimenting, testing which send days work best for readers like you, while also exploring new topic areas to ensure this newsletter continues to meet your needs. In a world where new models, frameworks, and breakthroughs arrive almost daily, staying up to date isn’t just nice to have, it’s essential. Whether you’re building, researching, or scaling AI systems, the difference between leading and lagging often comes down to who’s plugged into the right knowledge at the right time. That’s why we bring you DataPro: your weekly pulse on the launches, research, and tutorials shaping the field, curated with clarity, context, and links you can act on.This week’s lineup brings major releases and practical guides shaping AI and data:🔗 Hugging Face introduces AI Sheets - a no-code, local-first spreadsheet tool that makes dataset creation and enrichment as simple as typing a prompt.🔗 Salesforce AI releases Moirai 2.0 - a decoder-only transformer setting new benchmarks in time-series forecasting with smaller, faster, and more accurate models.🔗 Amazon unveils DeepFleet - a foundation model suite trained on billions of robot-hours to predict and optimize fleet traffic patterns in warehouses.🔗 Meet dots.ocr - a 1.7B parameter open-source vision-language model achieving state-of-the-art multilingual OCR and document parsing across 100+ languages.We also dive into tutorials that caught fire this week: Google’s Gemma 3 270M, built for hyper-efficient fine-tuning, and a hands-on guide to Model Predictive Control (MPC) using Python and CasADi.👉 A full-stack edition for builders, researchers, and thinkers who thrive on fresh ideas in data and AI,let’s unpack it.Cheers,Merlyn ShelleyGrowth Lead, PacktTop Tools Driving New Research 🔧📊🔵 Q2 2025 AI Hypercomputer updates: Google Cloud’s AI Hypercomputer is redefining scale: powering Gemini, Veo 3, and serving 980T+ tokens monthly. Highlights this quarter include Dynamic Workload Scheduler, Cluster Director upgrades, llm-d v0.2, and MaxText/MaxDiffusion improvements. Explore open frameworks, TPU/GPU scaling, and claim $300 free credit to simplify AI deployment and boost performance.🔵 How to Test an OpenAI Model Against Single-Turn Adversarial Attacks Using deepteam? Learn how to red team OpenAI models with deepteam, an open-source toolkit offering 10+ single-turn adversarial attacks including prompt injection, jailbreaking, leetspeak, Base64, and more. This hands-on guide shows how to install dependencies, set up your API key, define vulnerabilities, and test GPT-4o-mini against real-world adversarial prompts.🔵 Salesforce AI Releases Moirai 2.0: Salesforce’s Latest Time Series Foundation Model Built on a Decoder‑only Transformer Architecture. Salesforce AI Research introduces Moirai 2.0, a decoder-only transformer that tops GIFT-Eval benchmarks for time series forecasting. It’s 44% faster, 96% smaller, yet more accurate than Moirai_large. With multi-token prediction, advanced filtering, and diverse training data, it enables scalable forecasting across IT ops, sales, demand, and supply chain planning.🔵 Transform your data to Amazon S3 Tables with Amazon Athena: Amazon Athena now supports CTAS with S3 Tables, enabling serverless SQL-based data transformation with built-in Iceberg optimization, ACID transactions, and automatic maintenance. Easily migrate datasets (CSV, Parquet, JSON, etc.) into analytics-ready tables. The tutorial demonstrates transforming customer review data into S3 Tables, unlocking faster queries, simplified ETL, and robust enterprise-scale analytics.🔵 Estimating from No Data: Deriving a Continuous Score from Categories. This blog is about how to derive a continuous, fine-grained score from categorical outcomes when only labeled categories are available for training. It explains why standard classifiers fail to produce meaningful scores, and demonstrates how low-capacity networks with a linear bottleneck and category approximator head can generate interpretable, ordered risk scores.Topics Catching Fire in Data Circles 🔥💬🔵 Meet DeepFleet: Amazon’s New AI Models Suite that can Predict Future Traffic Patterns for Fleets of Mobile Robots. Amazon unveils DeepFleet, a suite of foundation models trained on billions of robot-hours to optimize warehouse fleets. Already enhancing operations across 300+ facilities, DeepFleet improves robot coordination, cuts congestion, and boosts efficiency by up to 10%. With RC, RF, IF, and GF architectures, it marks a leap in multi-robot forecasting.🔵 From Deployment to Scale: 11 Foundational Enterprise AI Concepts for Modern Businesses. A guide to 11 foundational AI concepts shaping enterprise adoption, from the integration gap and RAG reality to the agentic shift and feedback flywheel. The post highlights challenges like vendor lock-in, trust, and risk, while outlining how businesses can scale AI by embedding it natively and continuously reinventing processes.🔵 Meet dots.ocr: A New 1.7B Vision-Language Model that Achieves SOTA Performance on Multilingual Document Parsing. A new open-source vision-language model, dots.ocr (1.7B parameters), delivers state-of-the-art multilingual OCR and document parsing. Covering 100+ languages, it unifies layout detection and content recognition, preserves structure, and outputs JSON/Markdown/HTML. Benchmarks show it surpasses Gemini2.5-Pro in table accuracy and text precision, offering scalable, production-ready document analysis under the MIT license.🔵 Enhanced throttling observability in Amazon DynamoDB: Amazon DynamoDB introduces enhanced throttling observability, including structured exception messages with ThrottlingReasons, eight new CloudWatch metrics for detailed breakdowns, and a cost-efficient Contributor Insights mode that tracks only throttled keys. These features simplify diagnosing hot partitions, improving monitoring, and enabling faster mitigation of performance issues across tables and global secondary indexes.New Case Studies from the Tech Titans 🚀💡🔵 Smarter Authoring, Better Code: How AI is Reshaping Google Cloud's Developer Experience. Google Cloud is using Gemini-powered AI to accelerate documentation and code sample workflows. New systems auto-generate, validate, and test quickstarts and API code samples, ensuring accuracy and freshness at scale. By combining agentic AI systems with human oversight, Google delivers faster, more reliable developer guidance across evolving cloud services.🔵 A Coding Guide to Build and Validate End-to-End Partitioned Data Pipelines in Dagster with Machine Learning Integration: A step-by-step guide to building an end-to-end partitioned data pipeline in Dagster, integrating raw data ingestion, cleaning, feature engineering, validation checks, and model training. Using a custom CSV-based IOManager, daily partitions, and lightweight regression, the tutorial shows how to create reproducible, modular pipelines with structured outputs and integrated machine learning.🔵 Google AI Introduces Gemma 3 270M: A Compact Model for Hyper-Efficient, Task-Specific Fine-Tuning. Google AI introduces Gemma 3 270M, a 270M-parameter model built for hyper-efficient fine-tuning and on-device AI. With a 256k vocabulary, INT4 quantization, and strong instruction-following out of the box, it enables privacy-preserving, domain-specific applications. Compact yet powerful, it delivers energy-efficient inference, rapid customization, and production-ready deployment across mobile, edge, and enterprise environments.🔵 “My biggest lesson was realizing that domain expertise matters more than algorithmic complexity.“ This blog is about a data scientist’s journey from corporate ML to independent AI consulting, reflecting on real-world lessons from competitions, the importance of domain expertise over algorithmic complexity, and a problem-first approach to AI adoption. It also covers mentoring advice, career path choices in data/AI, and emerging trends like text-to-speech for language preservation.Blog Pulse: What’s Moving Minds 🧠✨🔵 Build a deep research agent with Google ADK: This guide shows how to build an agentic lead generation system using Google’s Agent Development Kit (ADK). By orchestrating cooperative agents for pattern discovery and lead generation, it demonstrates state management, parallel research, and dynamic validation, transforming brittle scripts into intelligent, scalable workflows that mimic a market research team.🔵 Hugging Face Unveils AI Sheets: A Free, Open-Source No-Code Toolkit for LLM-Powered Datasets. Hugging Face launches AI Sheets, a free, open-source, no-code tool that merges spreadsheets with LLM-powered data enrichment. Users can clean, transform, and generate datasets via prompts, using models like Qwen, Kimi, Llama 3, or custom local deployments. With built-in privacy, collaboration, and flexibility, it lowers barriers to AI-driven dataset creation.🔵 Building an MCP-Powered AI Agent with Gemini and mcp-agent Framework: A Step-by-Step Implementation Guide. A hands-on guide to building an MCP-powered AI agent with Gemini and the mcp-agent framework. The tutorial shows how to set up an MCP tool server, wire structured services like search, analysis, code execution, and weather, and integrate them with Gemini for asynchronous, extensible, and production-ready agent workflows.🔵 Model Predictive Control Basics: A step-by-step tutorial on Model Predictive Control (MPC) using Python and CasADi. It covers the fundamentals of MPC, formulates and solves an optimal control problem (OCP), and demonstrates implementation on a double integrator system. Includes full code, closed-loop simulations, and discussion of constraints, stability, and feasibility.See you next time! 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When it comes to cyberattacks, prevention is no longer enough. You must assume breach - but that doesn't mean that you can't fight back. With the right strategies and technologies in place, you can maintain patient care even during cyber attacks. Building a resilient cyber strategy has never been more important. Join us virtually for Rubrik’s Healthcare Summit on September 10th to gain vital insights into the challenges facing organizations and the future of cybersecurity in healthcare. Leaders like John Riggi, the National Advisor for Cybersecurity and Risk, American Hospitals Association will cover critical topics including: Day Zero: Navigating the Aftermath: Immediate steps post-cyberattack, exploring new recovery approaches beyond traditional methods. From Crisis to Continuity with the Minimum Viable Hospital: Learn to define and rapidly restore core applications critical for patient care continuity. 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Anthropic’s Persona Vectors, MCP Security Survival Guide, InfiniBand vs RoCEv2Become an AI Generalist that makes $100K (in 16 hours)One of the biggest IT giants, TCS laid off 12,000 people this week. And this is just the beginning of the blood bath. In the coming days you’ll see not thousands, but millions of more layoffs & displacement of jobs. So what should you do right now to avoid getting affected? Invest your time in learning about AI. The tools, the use cases, the workflows – as much as you can.Join the World’s First 16-Hour LIVE AI Upskilling Sprint for professionals, founders, consultants & business owners like you. Register Now (Only 500 free seats)Date: Saturday and Sunday, 10 AM - 7 PM.Rated 4.9/5 by global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours & 5 sessions, you will:✅ Learn how AI really works by learning 10+ AI tools, LLM models and their practical use cases.✅ Learn to build and ship products faster, in days instead of months✅ Build AI Agents that handle your repetitive work and free up 20+ hours weekly✅ Create professional images and videos for your business, social media, and marketing campaigns.✅ Turn these AI skills into10$k income by consulting or starting your own AI services business.All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. 🤯 🚀$5100+ worth of AI tools across 2 days — Day 1: 3000+ Prompt Bible, Day 2: Roadmap to make $10K/month with AI, additional bonus: Your Personal AI Toolkit Builder.Register Now (Only 500 free seats)SponsoredSubscribe|Submit a tip|Advertise with usWelcome to DataPro 144: Designing for IntelligenceThe data world is shifting fast, from dashboards and notebooks to agents that reason, write code, and navigate virtual worlds. In this issue, we look at what it means to design not just with AI, but for AI: platforms, workflows, and visualizations that collaborate, adapt, and inform with intelligence.We explore the tools reshaping how we build, the models pushing open boundaries, and the quiet craft of designing dashboards that speak clearly in a noisy world.🔍 Key Highlights This Issue:📓 AI-First Colab Notebooks: Google’s Data Science Agent in Colab Enterprise (BigQuery + Vertex AI) turns prompts into pipelines, coding, debugging, and visualizing in real-time.🤖 Gemini Code Assist: GitHub PRs meet Gemini 2.5, think code reviews with instant summaries, bug detection, and smart suggestions built-in.🛡️ MCP Security Survival Guide: Why agentic systems like MCP demand new security thinking. A breakdown of real-world exploits and how to avoid them.🧠 Anthropic’s Persona Vectors: Mapping and moderating LLM behavior, new research shows how traits like sycophancy or hallucination can be tracked and controlled during training.🔌 InfiniBand vs. RoCEv2: A practical guide to choosing your AI network stack. Scale performance isn't just about GPUs, it’s how fast they talk to each other.📊 Tableau Dashboard Design: Not all dashboards are created equal. A deep dive into four design strategies, guided, exploratory, scorecard, narrative, from Learning Tableau 2025.🧪 Post-Processing Beats Modeling? Lessons from the Mostly AI synthetic data challenge, how smart sampling and refinement outperformed complex models.🧩 OpenAI’s gpt-oss Models: Open-weight LLMs that compete with proprietary ones. Reasoning, tool use, and safety, all on hardware you can actually run.🌍 Google DeepMind’s Genie 3: From video generation to real-time simulated worlds, Genie 3 makes AI environments interactive, consistent, and controllable.🌐 The Agentic Shift at Google Cloud: Not just tool, but agents, APIs, and foundations for a new AI-native enterprise. The data platform is becoming a thinking partner.As the boundaries between data, design, and intelligence blur, this is the moment to stay curious, stay critical, and explore what thoughtful, agentic systems can truly enable. Let’s build with intelligence, not just for it.Sponsored👉 Join Snyk’s Sonya Moisset on August 28 at 11:00AM ET to explore how to secure AI-powered development from code to deployment. Learn how to protect your SDLC, mitigate risks in vibe coding, and earn 1 CPE credit. Register today!👉 Webinar alert! Mobile experts from Bitrise and Embrace break down advanced CI/CD tips and real-user insights to help you speed up builds & deliver top-quality apps. Register here.Cheers,Merlyn ShelleyGrowth Lead, PacktThe Value of Thoughtful Dashboard Design in Tableau - by Ayushi BulaniIn the rush to build a new Tableau dashboard, it’s tempting to jump straight into charts and data. But taking a step back to define your dashboard’s purpose and strategy can make the difference between a report that confuses and one that doesn’t. Put simply, effective dashboards are rooted in clear objectives and an understanding of what your audience needs at a glance. (src)A common professional setting for Tableau users is the executives wanting quick insights without having to wade through noise, the analysts needing interactive exploration, and the broader audiences needing a narrative to make data relatable. A thoughtful dashboard design strategy aligns your Tableau visuals with these needs. (src) It ensures you’re not just throwing data on a page, but actually communicating the ideas. In the long run, a bit of planning on “dashboard strategy” saves time and elevates the impact of your work.Four approaches to dashboard designOne of the key insights from the upcoming book Learning Tableau 2025 is that there isn’t a one-size-fits-all approach to dashboard design. The book’s authors outline at least four common design approaches, each suited to different scenarios. Lightly adapted from Learning Tableau 2025, here are the four approaches and what they entail:🔹Guided Analysis – This approach guides the audience through the data to facilitate discovery. In practice, you lead viewers step-by-step so they can understand the data’s implications and arrive at clear actions. A guided dashboard often anticipates a specific analysis path – you’ve done the analysis and now walk the user through those findings in a logical sequence.🔹Exploratory – An exploratory dashboard is an open sandbox. It provides tools (filters, drill-downs, etc.) for the audience to explore the data on their own. The idea is that the data’s story may evolve over time, so you empower users to investigate trends and relationships themselves. This approach is common in self-service BI scenarios, where different users might have different questions.🔹Scorecard / Status Snapshot – This is all about at-a-glance information. A scorecard or status snapshot delivers a concise summary of key performance indicators (KPIs) and metrics. It’s the classic executive dashboard: think of a one-page layout with big numbers, up/down arrows, and color-coded indicators. The goal is quick problem identification and monitoring – no heavy narrative, just the vital signs of the business in one view.🔹Narrative – A narrative dashboard focuses on telling a story with the data. It guides the viewer through a beginning, middle, and end using visuals and text in a cohesive sequence. For example, you might show how a metric changed over time during a specific event (imagine illustrating the spread of a disease or the timeline of a marketing campaign). This approach adds context and commentary to data, making the insights memorable and compelling.(Extracted and adapted from Learning Tableau 2025 by Milligan et al.)Putting these approaches into practiceThese different approaches matter because of their impact. Matching your dashboard design to your audience’s needs can dramatically improve how your insights land. For instance, if your CEO just wants a daily health check of the business, a scorecard-style dashboard ensures they see all critical KPIs in seconds (and nothing more). If you’re presenting to stakeholders at a quarterly review, a narrative dashboard with a clear storyline might be more effective – it can walk them through performance drivers and outcomes in a logical flow. On the other hand, when you’re building tools for analysts or power users, an exploratory dashboard gives them the flexibility to ask their own questions about the data. And if you’ve conducted deep analysis yourself, a guided dashboard lets you package those insights into an interactive journey, so colleagues can essentially retrace your steps and findings.Keep in mind that these approaches aren’t mutually exclusive. Often, a well-crafted dashboard will blend elements of each. You might start with a snapshot overview up top (scorecard style), then provide interactive filters for deeper exploration, and perhaps include annotations or highlights to add a mini narrative. The key is to be deliberate: know when you’re trying to simply inform versus when you need to persuade or invite exploration. By aligning the design to the goal, you avoid the common pitfalls of cluttered or directionless dashboards.In today’s data-driven environment, dashboards are a staple of communication – and thoughtful design is what separates the mediocre from the truly effective. A bit of upfront strategy about how you present information pays off with dashboards that people actually use and understand. (src) Whether you’re guiding a user through a data story or letting them dive in themselves, choosing the right approach will ensure your Tableau work delivers value, not just charts.For those who want to dive deeper and see these principles in action, the book Learning Tableau 2025 is packed with practical examples and tips on building impactful dashboards. It’s a resource well worth exploring if you’re looking to sharpen your Tableau skills and design more thoughtful, effective dashboards. By approaching your next project with a clear strategy in mind, you’ll be well on your way to creating dashboards that not only look good, but drive smarter decisions in your organization.Want to design dashboards that communicate, not just display?Take the Tableau dashboard design quiz to find your weak point—and see how Learning Tableau 2025 can help you fix it. Take the quiz here!Then, pre-order your copy of Learning Tableau 2025 to learn how to apply guided analysis, exploratory tools, executive snapshots, and narrative techniques in real projects—so your dashboards deliver insight with impact.🛒 Pre-order here.⚡Latest Drops: Data, AI, and What’s Next🔶 AI First Colab Notebooks in BigQuery and Vertex AI: Colab Goes Agentic! Google’s new AI-first Colab Enterprise is more than a notebook, it’s your AI teammate. With agentic capabilities via the Data Science Agent, it plans, codes, debugs, visualizes, and iterates, all with human-in-the-loop control. Seamlessly integrated with BigQuery and Vertex AI, this signals Google’s bold move to make AI not just assistive, but collaborative in real data science workflows.🔶 Gemini Code Assist and GitHub AI code reviews: AI Code Reviews That Just Work. Gemini Code Assist turns pull requests into productivity boosters. Integrated into GitHub, it delivers instant PR summaries, flags bugs, and suggests improvements, all powered by Gemini 2.5. With contextual understanding, interactive feedback, and high trust suggestions, it’s more than automation, it’s collaboration. Teams like Delivery Hero are already seeing faster reviews, better code, and happier devs. Seems like the future of software quality is here, and it’s AI-reviewed.🔶 The MCP Security Survival Guide: Best Practices, Pitfalls, and Real-World Lessons: MCP Is Powerful. That’s Also Why It’s Dangerous.Agentic systems like MCP are revolutionizing AI workflows, but they’re also exposing critical security flaws. From OAuth mishaps to remote code exploits, real-world breaches show just how risky "plug-and-play" can be. Hailey Quach’s guide is an urgent call: use MCP, but use it wisely. This isn’t just best practice, it’s survival. A must-read for anyone building secure, agentic AI infrastructure.Source: TowardsDataScience🔶 Anthropic’s Persona Vectors: Monitoring and controlling character traits in language models. Why Your LLM Might Start Flattering You, or Worse. Anthropic’s new research on persona vectors reveals a breakthrough in tracking and controlling AI “personalities.” By isolating neural patterns tied to traits like sycophancy, hallucination, or even evil, developers can now monitor personality drift, prevent unwanted behavior during training, and flag risky datasets, without degrading performance. If AI character control is the next frontier, persona vectors might be our steering wheel.🔶 InfiniBand vs RoCEv2: Choosing the Right Network for Large-Scale AI. Choosing the Fast Lane for AI Scale. Training massive AI models isn’t just about powerful GPUs, it’s about how fast they talk. This guide breaks down InfiniBand vs RoCEv2, the two dominant network stacks powering GPU-to-GPU communication. InfiniBand offers unrivaled speed but at a premium. RoCEv2 rides Ethernet’s rails with careful tuning. If you’re building for scale, your network isn’t infrastructure, it’s a performance multiplier. Choose wisely.🔶 How I Won the “Mostly AI” Synthetic Data Challenge? Post-Processing for Synthetic Data Accuracy. A recent synthetic data competition highlighted the power of post-processing over model complexity. By oversampling, trimming, and iteratively refining generated data, one solution significantly improved distributional accuracy and sequence coherence. Techniques like IPF and group-level swapping outperformed ensemble modeling. The results suggest that aligning generation strategies with evaluation metrics, rather than relying solely on generative models, can be a more effective path to high-quality synthetic datasets.🔶 Introducing gpt-oss: OpenAI’s Step Toward Transparent AI: Open-Weight Models Are Growing Up. OpenAI’s release of gpt-oss-120b and gpt-oss-20b brings open-weight models closer to proprietary performance on reasoning and tool use tasks. Trained with techniques from internal frontier models, both models offer strong results across benchmarks like MMLU and HealthBench. With full customizability, modest hardware requirements, and a safety evaluation pipeline, gpt-oss models provide a flexible option for developers working on local inference, alignment research, or agentic workflows.🔶 Google DeepMind’s Genie 3: A new frontier for world models:Simulated Worlds Are Becoming Playable. Genie 3 pushes world models from static simulation to real-time interaction. Unlike earlier video generation models, it enables consistent, navigable environments at 24 FPS, complete with memory, interactivity, and controllable events. This represents a step toward open-ended training environments for agents, but also opens up new questions around scalability, fidelity, and alignment as these systems move from outputting video to becoming the world itself.🔶 New agents and AI foundations for data teams: Data Platforms Are Becoming Cognitive Partners. Google’s latest update positions the Data Cloud as more than infrastructure, it’s the operating system for agentic AI. With specialized data agents, unified transactional-analytical memory, and built-in reasoning, the traditional data stack is giving way to autonomous, collaborative intelligence. The shift isn’t just technical, it redefines how data work gets done, embedding agency and adaptability directly into the platforms that power decision-making at scale.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Torchvista, AWS Data Processing MCP Server, Amazon Q + DLC MCP, Streamlit + MCP, ChatGPT AgentBecome an AI Generalist that makes $100K (in 16 hours)Still don’t use AI to automate your work & make big $$? You’re way behind in the AI race. But worry not:Join the World’s First 16-Hour LIVE AI Upskilling Sprint for professionals, founders, consultants & business owners like you. Register Now (Only 500 free seats)Date: Saturday and Sunday, 10 AM - 7 PM.Rated 4.9/10 by global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.In just 16 hours & 5 sessions, you will:✅ Learn the basics of LLMs and how they work.✅ Master prompt engineering for precise AI outputs.✅ Build custom GPT bots and AI agents that save you 20+ hours weekly.✅ Create high-quality images and videos for content, marketing, and branding.✅ Automate tasks and turn your AI skills into a profitable career or business.All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. 🤯 🚀$5100+ worth of AI tools across 2 days — Day 1: 3000+ Prompt Bible, Day 2: Roadmap to make $10K/month with AI, additional bonus: Your Personal AI Toolkit Builder.Register Now (Only 500 free seats)SponsoredSubscribe|Submit a tip|Advertise with usWelcome to DataPro #143: From Bits to Brains - The Tools Driving the Next Wave of Intelligent Systems 🧠📡What if your database could talk back with charts, or your containers built themselves when you spoke? What if your AI agent could say “I don’t know” and actually mean it?This week, we dive into a new breed of tools designed not just to build smarter systems, but to understand, reason, and scale them. These aren’t just marginal upgrades, they’re foundational shifts in how we build and interact with AI.Start with Mitra: Amazon’s tabular foundation model that ditches real-world data for synthetic priors (think causal graphs + tree ensembles) and still manages SOTA across tabular benchmarks via in-context learning.Then check out Qwen3-Coder-480B-A35B-Instruct, a Claude-class code model with 256K native context and 1M with Yarn, engineered for repository-scale agentic reasoning.Want BI that speaks SQL and your language? Wren AI is your GenBI agent, natural language in, SQL and insights out, thanks to a semantic layer, LLM integrations, and plug-and-play APIs.Visual domains aren’t left out. Cosmos DiffusionRenderer from NVIDIA reinvents video re-lighting with neural inverse rendering, 70GB models, and GPU-optimized pipelines for stunning realism.If you’re building with agents, 7 MCP Best Practices are a must-read, from schema validation to Dockerized deployments to performance tuning at scale.Meanwhile, ChatGPT Agent blurs the line between reasoning and doing, browsing, coding, and summarizing, all on its own virtual machine.But let’s not forget the human side. How Not to Mislead with Your Data is a masterclass on spotting narrative bias in data storytelling, and the ethical stakes behind our charts.And yes, Cloud SQL meets Vertex AI now means vector search and Gemini are just SQL calls away. You can embed, search, and analyze, all inside your relational DB.In the wild, Streamlit + MCP brings it all together in a sleek client interface that lets users query DeepWiki or HuggingFace-backed agents via natural language, no frontend dev required.AWS Data Processing MCP Server takes that to an enterprise level, streamlining schema discovery, query generation, and job monitoring across Glue, Athena, and EMR, all via natural language.Then, go deep with Amazon Q + DLC MCP: a system that automates PyTorch/TensorFlow container orchestration with a single prompt. Think: “Deploy PyTorch for multi-node training”, and it just happens.Finally, DeepSeek R1 on Vertex AI means no GPUs needed, just an API call. Run it on-demand, serverless, pay-as-you-go, no infrastructure stress.Still thinking of attention heads asdot products? Transformers as Addition Machines reframes attention with mechanistic interpretation, revealing layer-by-layer logic circuits.Or maybe you prefer pictures, Torchvista lets you trace PyTorch forward passes as interactive graphs inside your notebook, a dream for debugging or demystifying hidden layers.Semantic communication is making machines communicate with meaning, not bits. It’s the end of false alarms and overfitting to known categories, and it's all because of the knowledge graphs that reason over context and uncertainty.And if you’re ready to start building today, Google Cloud’s top 25 guides are a treasure trove: from RAG, RLHF, and agent orchestration to CI/CD pipelines and multi-agent chat apps, code included, no excuses.We’re in the midst of a shift: From models that classify to systems that reason. From dashboards to agents. From pixels to meaning.This issue is your map. Dive in, experiment, build.Sponsored: Your data, built your way with Twilio Segment — a customer data platform designed to cut through the chaos, unify your stack, and free you to focus on innovation over integration. Learn more.Cheers,Merlyn ShelleyGrowth Lead, PacktTop Tools Driving New Research 🔧📊⏩ Mitra: Mixed synthetic priors for enhancing tabular foundation models. Amazon’s Mitra is a tabular foundation model (TFM) that uses in-context learning to generalize across tabular tasks without retraining. Pretrained on synthetic data from causal models and tree-based methods, rather than real-world data, Mitra achieves state-of-the-art results across benchmarks like TabRepo and TabArena. It’s open source via AutoGluon 1.4.⏩ Qwen/Qwen3-Coder-480B-A35B-Instruct · Qwen3-Coder-480B-A35B-Instruct is Qwen’s most advanced code model, delivering Claude Sonnet-level performance on agentic coding and browser-use tasks. It supports 256K token context (extendable to 1M), tool calling, and repository-scale understanding. Built with 480B parameters (35B active), it uses in-context prompting and excels at function-call reasoning, agent frameworks, and long-horizon completions.⏩ Wren AI is your GenBI Agent: Wren AI is a GenBI agent that lets you query databases in natural language to generate SQL, charts, and AI-driven insights instantly. It features a semantic layer for governed accuracy, integrates with top LLMs, supports embedding via API, and connects to major data sources. Fast setup, cloud and open-source options included.⏩ nv-tlabs/cosmos1-diffusion-renderer: Cosmos DiffusionRenderer is NVIDIA’s latest video diffusion framework for high-quality image and video de-lighting and re-lighting. Built on DiffusionRenderer and powered by Cosmos, it features neural inverse and forward rendering with significant improvements in realism and control. It supports GPU-efficient inference, 70GB models, and full relighting pipelines for both static images and dynamic videos.Topics Catching Fire in Data Circles 🔥💬⏩ 7 MCP Server Best Practices for Scalable AI Integrations in 2025: Model Context Protocol (MCP) servers are becoming essential for secure, scalable, and agentic AI integrations. This guide outlines 7 best practices, toolset design, proactive security, schema validation, local/remote testing, Docker packaging, performance tuning, and documentation, that reduce errors, boost developer adoption, and power industry-wide AI success across finance, healthcare, e-commerce, and more.⏩ ChatGPT Agent: Bridging Research and Action: ChatGPT Agent introduces a powerful leap in agentic AI: it can now think and act on your behalf using its own virtual computer, navigating websites, running code, analyzing data, and producing editable outputs like slides and spreadsheets. It integrates browsing, terminals, APIs, and tool access to complete complex real-world tasks autonomously.⏩ How Not to Mislead with Your Data-Driven Story? Data storytelling helps us understand the world, but it can also mislead. This piece explores how persuasive narratives, even with accurate data, can distort truth. It highlights narrative bias risks like selection, framing, and interpretation, and urges data professionals to balance emotional storytelling with clarity, ethics, and rigorous data literacy.⏩ Integrate your Cloud SQL for MySQL instance with Vertex AI and vector search: Google Cloud’s Cloud SQL for MySQL now supports vector embeddings and Vertex AI integration, empowering developers to run AI-powered search and analysis directly in SQL. You can generate, store, and search vector embeddings with native SQL functions, perform ANN search, and invoke Gemini or custom Vertex AI models to assess customer sentiment or predict behavior, all within your database.New Case Studies from the Tech Titans 🚀💡⏩ MCP Client Development with Streamlit: Build Your AI-Powered Web App. This tutorial walks you through building a Streamlit-based MCP client interface that connects to remote MCP servers like DeepWiki and HuggingFace. The client lets users input topics and receive AI-generated summaries or recommendations via OpenAI’s API. It covers setup, secure key handling, MCP tool integration, and UI design, enabling rapid, modular deployment of AI-powered web tools.⏩ Accelerating development with the AWS Data Processing MCP Server and Agent: The AWS Data Processing MCP Server simplifies complex analytics workflows by enabling AI-driven natural language interactions with services like AWS Glue, Athena, and EMR. Built on the Model Context Protocol (MCP), it abstracts multi-service orchestration, automating tasks like schema discovery, query generation, reporting, and monitoring. Developers can integrate it via Amazon Q CLI or Claude Desktop to streamline onboarding, accelerate insight generation, and enhance observability.⏩ Streamline deep learning environments with Amazon Q Developer and MCP: Amazon Q + the DLC MCP Server radically simplifies how AI/ML teams manage Deep Learning Containers. Instead of manually customizing, testing, and deploying DLCs for PyTorch or TensorFlow, developers can now use natural language via Amazon Q CLI to automate everything, from image selection to ECR deployment, distributed training, and environment troubleshooting. It turns container operations into secure, conversational workflows.⏩ Deepseek R1 is available for everyone in Vertex AI Model Garden: DeepSeek R1 is now available on Vertex AI’s Model-as-a-Service (MaaS) platform, enabling businesses to access this powerful open model without managing GPU infrastructure. With just a few clicks or API calls, teams can test and deploy DeepSeek via a serverless, pay-as-you-go model. Vertex AI handles security, scalability, and compliance, accelerating AI innovation with zero infrastructure overhead.Blog Pulse: What’s Moving Minds 🧠✨⏩ Transformers (and Attention) are Just Fancy Addition Machines: Mechanistic interpretation is a novel AI interpretability approach that goes beyond tools like SHAP and LIME by uncovering how neural networks compute, not just what features influence outputs. It traces how features are encoded and transformed across layers, especially in transformers. By reimagining multi-head attention as additive rather than concatenative, it enables circuit-level analysis of neuron behavior. This method reveals the internal logic of models, opening doors to deeper understanding, debugging, and trust in complex AI systems.⏩ Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks. Torchvista is an open-source tool for interactively visualizing the forward pass of PyTorch models inside web-based notebooks like Colab or Jupyter. Unlike static tools, it offers zoomable, modular graph views, supports error-tolerant partial visualizations, and requires just a one-line trace_model() call. It traces tensor flows and module hierarchies during forward execution and renders them as interactive, nested graphs using JS libraries like D3 and Graphviz, making complex models understandable, debuggable, and more accessible for iterative development and exploration.⏩ From Rules to Relationships: How Machines Are Learning to Understand EachOther? Semantic communication shifts focus from transmitting raw bits to conveying meaning, crucial in modern, machine-heavy networks. Traditional SKB systems compress messages via fixed categories, but fail in unfamiliar scenarios. Knowledge graph-based semantic communication fixes this by modeling relationships between entities, enabling contextual reasoning. This allows systems to intelligently handle edge cases (e.g., maintenance workers during off-hours) by inferring intent and suggesting verification over false alarms. Though graph systems require more compute and expertise, they vastly improve real-world accuracy, adaptability, and decision-making in noisy, dynamic environments.⏩ 25 top how-to guides for Google Cloud: The best way to learn AI is to build it, and Google Cloud now offers a curated collection of 25+ hands-on how-to guides to help you do just that. From deploying large models like Llama 3 and DeepSeek on high-performance infrastructure, to creating advanced gen AI apps, fine-tuning with RAG and RLHF, and integrating agents with real-world systems, this living resource accelerates your AI journey. Each guide includes code, tools, and best practices, ready to help you build smarter, faster, and at scale.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

NVIDIA’s Audio Flamingo 3, GoogleSQL’s new pipe syntax, MetaStone-S1, Fractional ReasoningAn Exclusive Look into Next Gen BI – Live WebinarDashboards alone aren’t cutting it. The market’s moving toward something new: data apps, live collaboration, and AI that works the way teams actually work.See what's driving the rise of Next Gen BI, how Sigma earned a top debut on the Gartner Magic Quadrant, and what’s next for our roadmap.Secure Your SpotSponsoredSubscribe|Submit a tip|Advertise with usWelcome to DataPro 142: Tools Driving Tomorrow’s Thinking 🔬📈In this edition, we spotlight the breakthrough tools, patterns, and practices that are reshaping research and production in AI and data science.From NVIDIA’s Audio Flamingo 3 pushing the frontier of multimodal reasoning, to Fractional Reasoning’s elegant solution to adaptive LLM compute, and MetaStone-S1’s bold performance claims, this week’s releases are not just incremental; they’re foundational. Meanwhile, Kiro is redefining the dev experience, merging agentic coding with production-readiness from day one.On the systems front, Amazon EKS now scales to 100K nodes, opening the door to AGI-class workloads. And GoogleSQL’s new pipe syntax is winning hearts in the SQL community for its clarity and composability. If you’ve ever loathed nested subqueries, this is your moment.For those making decisions about tooling, don’t miss our link on Foundation vs. Custom Models, a smart, grounded guide for teams navigating performance vs. control. Also featured: Amazon SageMaker’s new unified catalog, practical AutoML with AutoKeras/Keras Tuner, and a no-fuss walkthrough of deploying Streamlit apps to AWS.Lastly, we dive into deeper reflections: Strands Agents 1.0 brings multi-agent orchestration into the real world, and standout articles explore paradox pitfalls in metrics, and how data’s 40-year evolution is shaping AI’s next wave.Let’s get into it. ⬇️Cheers,Merlyn ShelleyGrowth Lead, PacktTop Tools Driving New Research 🔧📊🔵 nvidia/audio-flamingo-3 · Audio Flamingo 3 (AF3) is an open Large Audio-Language Model (LALM) by NVIDIA for research use, capable of reasoning across speech, sound, and music. It supports long audio inputs, multi-turn voice dialogue, and chain-of-thought reasoning, achieving state-of-the-art results on 20+ tasks through unified audio representation and extensive dataset training.🔵 Fractional Reasoning via Latent Steering Vectors Improves Inference Time Compute: Fractional Reasoning introduces a model-agnostic, training-free method to dynamically adjust LLM reasoning depth at inference. By scaling latent steering vectors, it tailors compute per input complexity, boosting accuracy and efficiency. Compatible with Best-of-N, majority vote, and self-reflection, it outperforms fixed prompts across GSM8K, MATH500, and GPQA benchmarks.🔵 MetaStone-AI/MetaStone-S1: MetaStone-S1 is a 32B-parameter reflective generative model that rivals OpenAI-o3-mini on math, code, and Chinese reasoning. It combines Long-CoT Reinforcement and Process Reward Learning for efficient, high-quality inference. MetaStone-S1 achieves deep reasoning while reducing policy model costs by 99%, enabling fast, accurate outputs across multiple benchmarks.🔵 Introducing Kiro: Kiro is an agentic IDE that turns AI prototypes into production-grade apps using spec-driven development. It auto-generates requirements, design docs, and implementation tasks, and uses hooks for event-based automation. With built-in test coverage, design clarity, and consistency checks, Kiro helps developers ship reliable software faster and with greater confidence.Topics Catching Fire in Data Circles 🔥💬🔵 Do You Really Need a Foundation Model? Not every use case needs a foundation model. This guide compares foundation and custom models across performance, cost, latency, and control. It offers a decision framework, practical examples, and hybrid strategies to help teams choose the right approach, balancing rapid prototyping with long-term scalability, privacy needs, and task-specific optimization.🔵 Automating Deep Learning: A Gentle Introduction to AutoKeras and Keras Tuner. This guide introduces AutoKeras and Keras Tuner, two AutoML tools that simplify deep learning. AutoKeras automates architecture and training, while Keras Tuner optimizes hyperparameters of custom models. Together, they streamline experimentation, reduce guesswork, and boost performance, ideal for tasks like image classification, tabular modeling, or rapid prototyping with minimal manual tuning.🔵 Amazon EKS enables ultra scale AI/ML workloads with support for 100K nodes per cluster: Amazon EKS now supports up to 100,000 nodes per cluster, enabling ultra-scale AI/ML workloads with 1.6M Trainium or 800K GPU instances. This breakthrough powers large model training, reduces operational costs, and preserves Kubernetes compatibility, paving the way for AGI-scale innovation through enhanced orchestration, resiliency, and open-source flexibility.🔵 Exploring pipe syntax real-world use cases: GoogleSQL's pipe syntax reimagines SQL with a linear, readable data flow using the |> operator. It simplifies complex queries, streamlines data pipelines, and improves log analysis clarity. By eliminating nested structures and enabling intuitive chaining, pipe syntax boosts productivity, maintainability, and accelerates insight generation across BigQuery and Cloud Logging workflows.New Case Studies from the Tech Titans 🚀💡🔵 How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes. This article unpacks how paradoxes like Simpson’s, the Accuracy Paradox, and Goodhart’s Law mislead both data science and LLM evaluation. It shows how surface-level metrics can distort truth, urging practitioners to embrace contextual, nuanced measurement, especially in BI and Retrieval-Augmented Generation, where incentives, imbalance, and aggregation errors can derail decision-making.🔵 What Can the History of Data Tell Us About the Future of AI? This sweeping 40-year history of data explores how shifts in storage, architecture, and business models have shaped intelligent systems. By tracing personal, public, and enterprise data, from PCs to cloud to AI, the piece reveals how incentives, infrastructure, and data ownership will determine the trajectory of AI’s future.🔵 Streamline the path from data to insights with new Amazon SageMaker Catalog capabilities: Amazon SageMaker now streamlines analytics with new integrations: QuickSight for in-studio dashboarding, S3 Access Grants for secure unstructured data sharing, and automatic onboarding of Glue Data Catalog datasets. These updates unify structured and unstructured data, accelerating workflows from raw data to insights, governed, discoverable, and ready for ML and BI use.Blog Pulse: What’s Moving Minds 🧠✨🔵 Deploy a Streamlit App to AWS: This hands-on guide walks you through deploying a Streamlit app on AWS using Elastic Beanstalk. It covers preparing your code, switching from Postgres to S3 for data, configuring AWS infrastructure, and managing deployment steps. Ideal for developers needing scalable, secure alternatives to public cloud endpoints like Streamlit Community Cloud.🔵 Accuracy Is Dead: Calibration, Discrimination, and Other Metrics You Actually Need. This guide challenges accuracy as a primary evaluation metric, urging data scientists to adopt deeper, problem-specific tools. It explores advanced classification metrics like ROC-AUC, log loss, and Brier score, and regression metrics like R², RMSLE, and quantile loss, emphasizing calibration, uncertainty, and decision-readiness over surface-level model performance.🔵 Introducing Strands Agents 1.0: Production-Ready Multi-Agent Orchestration Made Simple: Strands Agents 1.0 is a production-ready SDK for building multi-agent AI systems. It introduces primitives like Agents-as-Tools, Swarms, Graphs, and A2A support for inter-agent communication. With session persistence, async performance, and flexible model integration, Strands simplifies orchestration, scaling from prototype to production for complex, collaborative, and distributed agentic workflows.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

The Making of Getting Started with Taipy — Lessons for Every Data EngineerMaster AI in 16 hours & Become Irreplaceable before 2025 ends! 🚀The spooky season is here—candies, costumes, and fear is everywhere.But the real nightmare? It's not ghosts—it's job lossOver 71% of people believe AI will take their jobs by 2025. The anxiety is real: Are you good enough? Fast enough? Smart enough?But here's your treat this Halloween—a real solution to end the fearJoin the online 2-Day LIVE AI MASTERMIND by Outskill - a hands-on intensive training designed to make you an AI powered professional who can learn, earn and Build with AI.Usually $395, but as a part of their halloween sale 🎃, you can get in for completely FREE!Rated 9.8/10 by Trustpilot– an opportunity that makes you an AI Generalist who can build, solve & work on anything with AI, instead of fearing it.In just 16 hours & 5 sessions, you will:✅ Build AI agents that save up to 20+ hours weekly and turn time into money✅ Master 10+ AI tools that professionals charge $150/hour to implement✅ Launch your $10K+ AI consulting business in 90 days or less✅ Automate 80% of your workload and scale your income without working more hoursLearn strategies used by the biggest giants like Google, Amazon, Microsoft from their practitioners 🚀🔥🧠Live sessions- Saturday and Sunday🕜10 AM EST to 7 PM EST🎁 You will also unlock $5000+ in AI bonuses: prompt bibles 📚, roadmap to monetize AI 💰 and your personalised AI toolkit builder ⚙️️ — all free when you attend!Join in now, (we have limited free seats!)SponsoredSubscribe|Submit a tip|Advertise with UsWelcome to DataPro #154!Behind the Book: Eric Narro and the Making of Getting Started with TaipyThis edition continues from last week’s article by Eric Narro, where he explored how to bring your Python models to life with Taipy. Today, we’re going behind the scenes to see how his book Getting Started with Taipy evolved, from an idea sparked at PyCon France to a full-fledged Packt title.Eric’s journey is a story of curiosity, persistence, and the spark that turns passion projects into published work. As an Analytics Engineer, he not only builds data applications but also helps others bridge the gap between prototyping and production.If you missed last week’s piece, From Time Series to Chatbots: Bring Your Python Models to Life with Taipy, it’s worth a read — especially if you’re curious about how Taipy enables developers to turn data, models, and algorithms into dynamic, user-ready applications.And here’s a little something extra: for one week only, you can grab Getting Started with Taipy at 30% off (ebook) and 10% off (print).Let’s dive into the story behind the book and the lessons that shaped it.Cheers,Merlyn ShelleyGrowth Lead, PacktSponsored: Join Snyk on October 22, 2025 at DevSecCon25 - Securing the Shift to AI NativeJoin Snyk October 22, 2025 for this one-day event to hear from leading AI and security experts from Qodo, Ragie.ai, Casco, Arcade.dev, and more!The agenda includes inspiring Mainstage keynotes, a hands-on AI Demos track on building secure AI, Snyk's very FIRST AI Developer Challenge and more! Save your spot now.How I Got to Write a Book with Packt | My Personal Experience by Erric NarroIf you don’t have a Medium account, you can read this storyhere.Wow,I wrote a book!About a Python library, of all things. The book is calledGetting Started with Taipy,if you want to take a look at it.Would you have told me, 4 years ago: “Eric, you’re going to write a book about computer science or data topics, and you’ll actually be apublished author,” I’d have laughed and said, “Come on, stop lying to me!”But here we are.In this article, I want to sharehow I ended up writing a book: the story behind the opportunity, the twists and turns that led me there, and the lessons I picked up along the way. In a follow-up post, I’ll dive deeper into what it was really like to go through the writing and publishing process.The motivation for this article is to eventually motivate anyone reading this to take action and work hard towards their goals, whatever they are. A second motivation is to show how working towards your goals may end up giving you unexpected results (I never thought of writing a book before I was asked to do it!)I’m 38 as I write these lines. I guess any story behind any personal outcome could be traced back to birth, but don’t worry, I won’t torture you with a detailed overview of my past! Still, there’s a wholewaterfall of eventsthat led me here, and that’s what I want to write about.How I Became a Data AnalystFor a living, I have a job. I’m a data analyst. Although, to be honest, I actually do more of a data engineering role these days (with ETL tasks, integrating data into databases, and so on, it’s quite diversified).I’ve written before aboutthe path I took to becoming a data analyst, but to summarize: I was a vineyard technician for 8 years, I learned Python to build my own tools, eventually I learned programming more extensively in college with distance studies and through a number of Coursera courses, and I effectively changed careers after sometime programming both at my work, or by doing personal projects.It took me a long time to take the step of changing careers, in part because I started to learn programming (and program effectively) as a way to improve and automate tasks at my former job (which I didn’t dislike); also, at some point, there was some lack of confidence to make the switch. But over time, I realized that I loved programming and working with data even more than being a vineyard technician, and that gave me the push I needed.Before officially changing careers, I had already learnedversion control, built a small GitHubportfolio(with README files and documentation about them). I also gained a foundation inSQL. I dabbled in web development withPHPandMySQL. I knew how to deal withLinux systems. During college, I had programmed inC, Bash, LISP, JavaScript, and Prolog, and I knew my way around Boolean logic, encoding, and binary calculations. I also knew some about statistics, analytical workflows, and data warehousing.The reason I mention all this is: it was quite odd that I became a data analyst in the first place;I found that passion along the way. But at the same time, it wasn’t a miracle either;it was the result of hard work, and ultimately, it was the result of other people giving me a chance, based on what I was able to share with them. That is why it’s important tojust do things. Efforts will end up paying in one way or another. Byjust doing things, you’ll eventually end up doing too many things, and then you’ll have to choose which one… but until then, just start doing something you like!And well, I also mention this because there’s no way I would have found out about Taipy, which is a Python library, had I not been proficient with Python!How I Discovered TaipyMy first (and current) position as a data analyst was (and is) for a contracting company. My client — then and now — is an insurance company located near Nice. Americans like to call that area theFrench Riviera, which sounds super fancy… but I was working remotely from Bergerac, which, let’s be honest, sounds more like a guy with a big nose. Jokes aside, Bergerac is a small town not far from Bordeaux.And once again, things got a little odd.It was February 16, 2023. I was at work (well, working from home), researching a Python library, when an ad popped up on the documentation site that caught my attention: that very weekend,PyCon France was taking place in Bordeaux. I hadn’t heard about it before, but I immediately booked a hotel night and decided to go.Had I not seen that small ad, or had the conference been in some other city, I wouldn’t have attended it.Not only did I learn a ton at the conference, but it also gave me plenty ofmaterial to write about on Medium, where I had just started publishing. The timing couldn’t have been more perfect. So yes, it was odd, but also a reminder that luck tends to meet you halfway,afteryou’ve put in the work. I attended PyCon as a junior programmer, sure, but a programmer nonetheless!Continue reading the full article on our Packt Medium Handle here.Here’s a quick throwback to last week’s piece for those who didn’t get a chance to read it.Meet Taipy: A Pure-Python, Fast, and Scalable Application BuilderBy Eric NarroFrom Time Series to Chatbots: Bring your Python Models to Life with TaipyTaipy is a Python application builder with one clear promise:deploy your data applications in real production environments. It’s the ideal tool for creating scalable, interactive apps that bring your models, analytics, and algorithms to life. Whether you’re building dashboards, optimization tools, or AI-powered chatbots, Taipy helps data professionals turn prototypes into powerful, end-user applications. WithGetting Started with Taipy, you’ll learn how to build complete applications from the ground up, deploy them confidently, and explore real-world examples and advanced use cases that showcase Taipy’s full potential.Python has long been the go-to language for data professionals, not because they’re developers, butbecause Python makes complex work accessible.Analysts, data scientists, and AI engineers use it to model data, run analytics, and visualize results.But when it comes to turning those models into real applications for end users, things get tricky. Building a web app the traditional way, with backend frameworks, databases, and front-end stacks, is often out of reach for data teams. It demands skills, time, and coordination that slow everything down and increase costs.Tools like Power BI or Tableau help visualize data, but they can’t trulyrunPython code or offer the flexibility of a full application. Python frameworks like Streamlit, Dash, Panel, or Gradio solve the problem partially. Each has trade-offs. To give an example, Streamlit is a great library for prototyping: it’s very easy to learn, and you can create demos in no time. While you can take Streamlit applications to production, they are harder to scale because they don’t optimize the way code runs, and they run on their own server (you can’t run them in a WSGI server). What this means is you can create useful applications for end users if they make limited use of the app, or if you don’t need to process large amounts of data.That’s where Taipy comes in!Taipy lets you create scalable, production-grade applications directly in Python.Whether for time series, optimization, geospatial analysis, or even LLM chatbots, Taipy is designed for performance and scalability.You can deploy Taipy apps on WSGI servers, handle multiple users efficiently, and still build everything using pure Python.Continue reading the full article on our Packt Medium Handle here.See you next time!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}