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

Building and deploying GenAI applications with Vertex AI

Now, let’s see how you can use Vertex AI GenAI features programmatically and integrate them with your apps.

Use case 1 – using GenAI models to extract key entities from scanned documents

We will use a publicly available patent document from the US Patents and Trademark Office as a sample document and extract the following information from the document:

  • Inventor name
  • Location of the inventor
  • Patent number

Refer to the notebook at https://github.com/PacktPublishing/The-Definitive-Guide-to-Google-Vertex-AI/blob/main/Chapter12/Chapter12_Vertex_GenAI_Entity_Extraction.ipynb

In this notebook, you will perform the following steps to extract the required information:

  1. Extract the text from the document by using the Document AI Optical Character Recognition (OCR) tool.
  2. Feed the text to the GenAI model (text-bison) along with a detailed prompt about the entities we need to extract...