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

Packaging a model to submit it to Vertex AI as a training job

The previous section demonstrated a small image colorization experiment on a Vertex AI Workbench notebook. Notebooks are great for small-scale and quick experiments, but when it comes to large-scale experiments (with more compute and/or memory requirements), it is advised to launch them as a Vertex AI job and specify desired machine specifications (accelerators such as GPU or TPU if needed) for optimal experimentation. Vertex AI jobs also let us execute tons of experiments in parallel without waiting for the results of a single experiment. Experiment tracking is also quite easy with Vertex AI jobs, so it becomes easier to compare your latest experiments with past experiments with the help of saved metadata and the Vertex AI UI. Now, let’s use our model experimentation setup from the previous section and launch it as a Vertex AI training job.

Important note

Vertex AI jobs run in a containerized environment,...