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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

Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs

Machine learning (ML) projects are complex in nature and require an entirely different type of development environment from normal software applications. When the data is huge, a data scientist may want to use several big data tools for quick wrangling or preprocessing needs, and a deep learning (DL) model might require several GPUs for fast training and experimentation. Additionally, dedicated compute resources are required for hosting models in production, and even more to scale them up to the enterprise level. Acquiring such resources and tools is quite costly, and even if we manage to buy and set things up, it takes a lot of effort and technical knowledge to bring them together into a project pipeline. Even after doing all that, there are risks of downtime and data security.

Nowadays, cloud-based solutions are very popular and take care of all the technical hassle, scaling, and security aspects for us....

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