Book Image

The Definitive Guide to Google Vertex AI

By : Jasmeet Bhatia, Kartik Chaudhary
4 (1)
Book Image

The Definitive Guide to Google Vertex AI

4 (1)
By: Jasmeet Bhatia, Kartik Chaudhary

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)
1
Part 1:The Importance of MLOps in a Real-World ML Deployment
4
Part 2: Machine Learning Tools for Custom Models on Google Cloud
14
Part 3: Prebuilt/Turnkey ML Solutions Available in GCP
18
Part 4: Building Real-World ML Solutions with Google Cloud

Summary

In this chapter, we delved into the world of XAI and its relevance in modern MLOps. We discussed how XAI aids in building trust, ensuring regulatory compliance, debugging and improving models, and addressing ethical considerations.

We explored different explanation techniques for various types of data, including tabular, image, and text data. Techniques such as LIME, SHAP, permutation feature importance, and others were discussed for tabular data. For image data, methods such as Integrated Gradients and XRAI were explained, while text-specific LIME was presented for text data.

This chapter also provided an overview of the XAI features available in GCP, including both feature-based and example-based explanations.

At this point, you should have gained a good understanding of XAI, its importance, various techniques, and practical applications in the context of Vertex AI. As the field of AI continues to evolve, the role of XAI in creating transparent, trustworthy, and...