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The Definitive Guide to Google Vertex AI
By :

The Definitive Guide to Google Vertex AI
By:
Overview of this book
While AI has become an integral part of every organization today, the development of large-scale ML solutions and management of complex ML workflows in production continue to pose challenges for many. Google’s unified data and AI platform, Vertex AI, directly addresses these challenges with its array of MLOPs tools designed for overall workflow management.
This book is a comprehensive guide that lets you explore Google Vertex AI’s easy-to-advanced level features for end-to-end ML solution development. Throughout this book, you’ll discover how Vertex AI empowers you by providing essential tools for critical tasks, including data management, model building, large-scale experimentations, metadata logging, model deployments, and monitoring. You’ll learn how to harness the full potential of Vertex AI for developing and deploying no-code, low-code, or fully customized ML solutions. This book takes a hands-on approach to developing u deploying some real-world ML solutions on Google Cloud, leveraging key technologies such as Vision, NLP, generative AI, and recommendation systems. Additionally, this book covers pre-built and turnkey solution offerings as well as guidance on seamlessly integrating them into your ML workflows.
By the end of this book, you’ll have the confidence to develop and deploy large-scale production-grade ML solutions using the MLOps tooling and best practices from Google.
Table of Contents (24 chapters)
Preface
Part 1:The Importance of MLOps in a Real-World ML Deployment
Chapter 1: Machine Learning Project Life Cycle and Challenges
Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team?
Part 2: Machine Learning Tools for Custom Models on Google Cloud
Chapter 3: It’s All About Data – Options to Store and Transform ML Datasets
Chapter 4: Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs
Chapter 5: No-Code Options for Building ML Models
Chapter 6: Low-Code Options for Building ML Models
Chapter 7: Training Fully Custom ML Models with Vertex AI
Chapter 8: ML Model Explainability
Chapter 9: Model Optimizations – Hyperparameter Tuning and NAS
Chapter 10: Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines
Chapter 11: MLOps Governance with Vertex AI
Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
Chapter 12: Vertex AI – Generative AI Tools
Chapter 13: Document AI – An End-to-End Solution for Processing Documents
Chapter 14: ML APIs for Vision, NLP, and Speech
Part 4: Building Real-World ML Solutions with Google Cloud
Chapter 15: Recommender Systems – Predict What Movies a User Would Like to Watch
Chapter 16: Vision-Based Defect Detection System – Machines Can See Now!
Chapter 17: Natural Language Models – Detecting Fake News Articles!
Index
Customer Reviews