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

ML Model Explainability

In the rapidly evolving world of machine learning (ML) and artificial intelligence (AI), developing models capable of delivering accurate predictions is no longer the sole objective. As organizations increasingly rely on data-driven decision-making, understanding the rationale behind a model’s predictions becomes paramount. The growing need for explainability in ML models stems from ethical, regulatory, and practical concerns, and it is here that the concept of Explainable AI (XAI) comes into play.

This chapter delves into the intricacies of Explainable ML models, a critical component in the MLOps landscape, with a focus on their implementation in the Google Cloud ecosystem. Although a comprehensive exploration of XAI techniques and tools is beyond this chapter’s scope, we aim to equip you with the knowledge and skills to build transparent, interpretable, and accountable ML models using the Explainable ML tools available on GCP.

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