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The Definitive Guide to Google Vertex AI

You're reading from  The Definitive Guide to Google Vertex AI

Product type Book
Published in Dec 2023
Publisher Packt
ISBN-13 9781801815260
Pages 422 pages
Edition 1st Edition
Languages
Authors (2):
Jasmeet Bhatia Jasmeet Bhatia
Profile icon Jasmeet Bhatia
Kartik Chaudhary Kartik Chaudhary
Profile icon Kartik Chaudhary
View More author details

Table of Contents (24) Chapters

Preface 1. Part 1:The Importance of MLOps in a Real-World ML Deployment
2. Chapter 1: Machine Learning Project Life Cycle and Challenges 3. Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team? 4. Part 2: Machine Learning Tools for Custom Models on Google Cloud
5. Chapter 3: It’s All About Data – Options to Store and Transform ML Datasets 6. Chapter 4: Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs 7. Chapter 5: No-Code Options for Building ML Models 8. Chapter 6: Low-Code Options for Building ML Models 9. Chapter 7: Training Fully Custom ML Models with Vertex AI 10. Chapter 8: ML Model Explainability 11. Chapter 9: Model Optimizations – Hyperparameter Tuning and NAS 12. Chapter 10: Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines 13. Chapter 11: MLOps Governance with Vertex AI 14. Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
15. Chapter 12: Vertex AI – Generative AI Tools 16. Chapter 13: Document AI – An End-to-End Solution for Processing Documents 17. Chapter 14: ML APIs for Vision, NLP, and Speech 18. Part 4: Building Real-World ML Solutions with Google Cloud
19. Chapter 15: Recommender Systems – Predict What Movies a User Would Like to Watch 20. Chapter 16: Vision-Based Defect Detection System – Machines Can See Now! 21. Chapter 17: Natural Language Models – Detecting Fake News Articles! 22. Index 23. Other Books You May Enjoy

No-Code Options for Building ML Models

In recent years, the world of machine learning has undergone a profound transformation, breaking free from the realm of expert data scientists and engineers to empower a broader audience. The rise of no-code machine learning platforms has ushered in a new era, where individuals with diverse skill sets and backgrounds can harness the power of artificial intelligence to solve complex challenges, without writing a single line of code. This democratization of machine learning has not only expedited the development process but has also opened up a myriad of opportunities for businesses and individuals alike.

In this chapter, we will dive into the foundations of no-code machine learning, shedding light on the remarkable tools and services Google Cloud Vertex AI offers. We will explore how users can leverage prebuilt machine learning models, AutoML capabilities, and visual interfaces to construct sophisticated and highly accurate models with ease...

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