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

How can Vertex AI help with implementing MLOps?

Google Cloud Vertex AI is a platform that provides tools and resources for the end-to-end implementation of the ML development life cycle.

Vertex AI can help with MLOps by providing features such as automated model building and deployment, model versioning and tracking, and monitoring and managing models in production. Additionally, it provides tools for collaboration and shared access to resources, allowing teams to work together on large and distributed ML projects.

Vertex AI can also help with other aspects of MLOps, such as the following:

  • Data management: Vertex AI can help with data preparation, labeling, and management, which are crucial for building accurate ML models
  • Experimentation: Vertex AI can help track and manage experiments, including comparing and selecting the best models
  • Model governance: Vertex AI can help manage model access and permissions and monitor models for drift and compliance
  • Continuous...