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

Overview of existing Document AI processors

As discussed previously, the Document AI platform provides prebuilt parsers for general-purpose, as well as some specialized, use cases. As these processors are prebuilt, they are readily available to use in any relevant use case with very little effort. Before jumping into an example of how these processors work, let’s first look at the list of available processors as part of Google Cloud’s Document AI platform:

  • Document OCR: Identify and extract both machine-printed as well as handwritten text from documents in over 200 languages
  • Form Parser: Extract key-value pairs (entity and checkbox), tables, and generic entities in addition to OCR text
  • Intelligent Document Quality Processor: Assesses the quality of documents based on their readability and provides a quality score
  • Document Splitter: Automatically splits documents based on logical boundaries

Document AI provides us with numerous specialized processors...