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

Tools in Vertex AI that can help with governance

Vertex AI offers several tools to help with ML solution governance and monitoring that you can utilize to implement and track your organization’s standard governance policies and more generic governance best practices. Please keep in mind that for many of the governance policies, especially the ones around security and cost management, you will need to use tools outside of Vertex AI. For example, to set up monthly cost limits and budgets, you will need to use GCP’s native billing tools.

Let’s walk through the details of the different tools within Vertex AI that can be used as part of MLOps governance processes:

Model Registry

Vertex AI Model Registry provides a centralized, organized, and secure location for managing all ML models within an organization. This facilitates seamless and efficient ML operations, from development and validation to deployment and monitoring:

Figure 11.1 – Vertex AI Model Registry

Figure 11...