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

Deploying a model in Vertex AI

Now, let us walk you through the steps of deploying the trained model on Vertex AI to enable real-time predictions:

  1. Go to Model Registry, click on the model and then the model version you want to deploy, and on the DEPLOY & TEST tab, click DEPLOY TO ENDPOINT.
Figure 5.18 – Initiating model deployment

Figure 5.18 – Initiating model deployment

  1. Type in the desired name of the API endpoint being created and click CONTINUE.
Figure 5.19 – Creating a model endpoint

Figure 5.19 – Creating a model endpoint

  1. You can leave all default options unchanged for quick test deployment, but these are the settings you need to understand:
    • Traffic split: If multiple versions of the model are deployed on the same API endpoint, this option allows users to define what percentage of total traffic is allocated to a specific version. For example, when deploying a new model, you might want only 2% of the overall incoming data to be routed to the new model so that...