DataPro

08 Sep 2025

Real-World Lessons From 50+ Agentic Orchestration Projects, Gemini Cloud Assist for Spark, NetoAI’s TSLAM: First Open-Source Telecom LLM, ARGUS Recommender

08 Sep 2025

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Merlyn from Packt

24 Sep 2025

Packt Live: Algo Trading Workshop With Jason Strimpel

Merlyn from Packt

24 Sep 2025

Here’s why this live session on algo trading could be the perfect add-on to your data toolkit.📢 A Packt Live Session You Shouldn’t MissWe wanted to share an upcoming Packt Live workshop that we believe will resonate with many of you in the DataPro community.On September 27, Jason Strimpel,author of Python for Algorithmic Trading Cookbook and founder of PyQuant News, is hosting a 2.5-hour hands-on workshop on Algorithmic Trading with Python.Why should you care as a data professional? Because algo trading is a natural extension of your skills. You already work with data to generate insights. This session shows you how those same skills can be applied to the financial markets: turning data into signals, testing strategies safely, and deploying systems that run live.Here’s what you’ll explore live with Jason:✅ Backtesting strategies with VectorBT✅ Prototyping and validating in pandas✅ Deploying trading systems via the Interactive Brokers API✅ Managing execution risks like slippageLEARN WITH JASON LIVEWhen you register, you’ll instantly unlock:📘 Python for Algorithmic Trading Cookbook eBook🛠️ Two bonus setup guides to install Python libraries with ease💬 Private Discord access to post queries, get direct answers from Jason, and join peer-learningAnd after the workshop, you’ll receive:🎥 90-day replay access to revisit the full session📜 A certificate of completion to showcase your achievementIn today’s AI-driven job market, adding algo trading to your toolkit isn’t just about finance. It’s about broadening your ability to apply data in real-world, high-impact domains.⚡ Seats are limited, consider this your heads-up to secure a spot.BUILD TRADING SYSTEMS LIVE WITH JASON📅 Date: September 27, 2025⏰ Duration: 2.5 hours (Workshop + Q&A)💻 Format: Live & Online + Private DiscordSee you at the workshop!Cheers,Merlyn Shelley,Growth Lead @Packt.*{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}}

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Merlyn from Packt

04 Sep 2025

DataPro Expert Insight: Agentic AI: The Next Leap in Intelligent Systems

Merlyn from Packt

04 Sep 2025

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}}

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Merlyn from Packt

21 Aug 2025

DataPro Expert Insight: Data Products – Turning Data into Tangible Value

Merlyn from Packt

21 Aug 2025

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}}

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Real-World Lessons From 50+ Agentic Orchestration Projects, Gemini Cloud Assist for Spark, NetoAI’s TSLAM: First Open-Source Telecom LLM, ARGUS Recommender

Packt Live: Algo Trading Workshop With Jason Strimpel

DataPro Expert Insight: Agentic AI: The Next Leap in Intelligent Systems

DataPro Expert Insight: Data Products – Turning Data into Tangible Value

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