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 the chapter, we learned how to work with a Vertex AI-based managed training environment and launch custom training jobs. Launching custom training jobs on Vertex AI comes with a number of advantages, such as managed metadata tracking, no need to actively monitor jobs, and the ability to launch any number of experiments in parallel, choose your desired machine specifications to run your experiments, monitor training progress and results in near-real time fashion using the Cloud console UI, and run managed batch inference jobs on a saved model. It is also tighly integrated with other GCP products.

After reading this chapter, you should be able to develop and run custom deep learning models (using frameworks such as TensorFlow) on Vertex AI Workbench notebooks. Secondly, you should be able to launch long-running Vertex AI custom training jobs and also understand the advantages of the managed Vertex AI training framework. The managed Google Cloud console interface and TensorBoard...