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

Vertex AI – Generative AI Tools

Generative artificial intelligence (GenAI) is a rapidly evolving field of AI that enables machines to create new content, such as text, images, code, and music. GenAI models are trained on massive datasets of existing content, and they learn to identify patterns and relationships that underlie that content. Once trained, these models can be used to generate new content that is similar to the content they were trained on but that is also unique and creative.

GenAI models can be used for a wide variety of applications, including the following:

  • Text generation: Generating text, such as news articles, blog posts, marketing copy, and creative content
  • Chatbots: Creating chatbots that can have natural conversations with users
  • Image generation: Generating images, such as product photos, marketing images, and artistic images
  • Code generation: Generating code, such as Python scripts, Java classes, and HTML templates
  • Text embeddings...