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

Creating custom Document AI processors

If we are unable to find a suitable prebuilt processor for our use case, Document AI Workbench lets us build and train our own tailored processors from scratch and with minimal effort. If we go to the Workbench tab inside Document AI, we’ll get the following options for creating a custom processor (see Figure 13.7):

Figure 13.7 – Document AI Workbench for creating custom model-based processors

Figure 13.7 – Document AI Workbench for creating custom model-based processors

In this exercise, we will work with the Custom Document Extractor solution to create a custom processor. Once we click on CREATE PROCESSOR, we will be able to find this processor within the My Processors tab. If we click on the processor, we will get options for training, evaluating, and testing our custom processor, as well as options for managing deployed versions of custom models. After training a version, we can also configure the Human-in-the-loop feature. See Figure 13.8 for these options:

Figure 13.8 – Custom Document AI processor details in the Google Cloud console UI...