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

NAS on Vertex AI overview

Vertex AI NAS is an optimization technique that can be leveraged to find the best neural network architecture for a given ML use case. NAS-based optimization searches for the best network in terms of accuracy but can also be augmented with other constraints such as latency, memory, or a custom metric as per the requirements. In general, the search space of possible neural networks can be quite large and NAS may support a search space as large as 10^20. In the past few years, NAS has been able to successfully generate some state-of-the-art computer vision network architectures, including NASNet, MNasNet, EfficientNet, SpineNet, NAS-FPN, and so on.

It may seem complex, but NAS features are quite flexible and easy to use. A beginner can leverage prebuilt modules for search spaces, trainer scripts, and Jupyter notebooks to start exploring Vertex AI NAS on a custom dataset. If you are an expert, you could potentially develop custom trainer scripts, custom search...