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 have learned about two popular ML workflow orchestration tools – Vertex AI Pipelines (managed Kubeflow) and Cloud Composer (managed Airflow). We have also implemented a Vertex Pipeline for an example use case, and similarly, we have also developed and executed an example DAG with Cloud Composer. Both Vertex AI Pipelines and Cloud Composer are managed services on GCP and make it really easy to set up and launch complex ML and data-related workflows. Finally, we have learned about getting online and batch predictions on Vertex AI for our custom models, including some best practices related to model deployments.

After reading this chapter, you should have a good understanding of different ways of carrying out ML workflow orchestration on GCP and their similarities and differences. Now, you should be able to write your own ML workflows and orchestrate them on GCP via either Vertex AI Pipelines or Cloud Composer. Finally, you should also be confident...