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

Monitoring model training progress

In the previous section, we saw how easy it is to launch a Vertex AI custom training job with desired configurations and machine types. These Vertex AI training jobs are really useful for running large-scale experiments where training uses high compute (multiple GPUs or TPUs) and also may run for a few days. Such long-running experiments are not very feasible to run in a Jupyter Notebook-based environment. Another great thing about launching Vertex AI jobs is that all the metadata and lineage are tracked in a systematic way so that we can come back later and look into our past experiments and compare them with the latest ones in an easy and accurate way.

Another important aspect is monitoring the live progress of training jobs (including metrics such as loss and accuracy). For this purpose, we can easily set up Vertex AI TensorBoard within our Vertex AI job and track the progress in a near real-time fashion. In this section, we will set up a TensorBoard...