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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

ad hoc transformation

categorical data, handling 45

numeric data, handling 44

within Jupyter Notebooks 43

AI techniques 165

global explainability, versus local explainability 165

tabular data techniques 168

text data techniques 170

area under the ROC curve (ROC-AUC) 371

artificial intelligence (AI) 3, 49, 133, 163, 241, 304

Artificial Neural Networks (ANNs) 207

Atomicity, Consistency, Isolation and Durability (ACID) 42

attention mechanisms 171

AUC PR 85

AUC ROC 85

AutoML 68, 306

AutoML for tabular data

SHAP-based explanation, using 173

AutoML for Text Analysis 313

classification 314

entity extraction 314

sentiment analysis 314

AutoML Translation 309-312

AutoML Video Intelligence 308

use cases 308

B

batch normalization...