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The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI

By : Jasmeet Bhatia, Kartik Chaudhary
4.9 (8)
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The Definitive Guide to Google Vertex AI

The Definitive Guide to Google Vertex AI

4.9 (8)
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)
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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

Model Optimizations – Hyperparameter Tuning and NAS

We have now become quite familiar with some of the Vertex AI offerings related to managing data, training no-code and low-code models, and launching large-scale custom model training jobs (with metadata tracking and monitoring capabilities). As ML practitioners, we know that it is highly unlikely that the first model we train would be the best model for a given use case and dataset. Thus, in order to find the best model (which is the most accurate and least biased), we often use different model optimization techniques. Hyperparameter Tuning (HPT) and Neural Architecture Search (NAS) are two such model optimization techniques. In this chapter, we will learn how to configure and launch model optimization experiments using Vertex AI on Google Cloud.

In this chapter, we will first learn about the importance of model optimization techniques such as HPT and then learn how to quickly set up and launch HPT jobs within Google Vertex...

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