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

What Is MLOps, and Why Is It So Important for Every ML Team?

Machine learning operations (MLOps) is a pivotal practice for modern ML teams, encompassing the blend of technological and operational best practices. At its heart, MLOps seeks to address the challenges of productionizing ML models and fostering better collaboration between data scientists and IT teams. With the rapid advancements in technology and increasing reliance on ML solutions, MLOps is becoming the backbone of a sustainable and scalable ML strategy. This chapter will delve deep into the essence of MLOps, detailing its significance, its various maturity levels, and the role of Google’s Vertex AI in facilitating MLOps. By the end of this chapter, you will be equipped with a robust understanding of MLOps principles and what tools in Vertex AI can be used to implement those principles.

In this chapter, we will cover the following topics:

  • Why is MLOps important?
  • MLOps maturity levels
  • How can Vertex...