You're reading from The Definitive Guide to Google Vertex AI Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices

Product type Paperback

Published in Dec 2023

Publisher Packt

ISBN-13 9781801815260

Length 422 pages

Edition 1st Edition

Tools

MLOps

Concepts

Data Science

Authors (2):

Kartik Chaudhary

Jasmeet Bhatia

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Table of Contents (24) Chapters

Preface

1. Part 1:The Importance of MLOps in a Real-World ML Deployment

2. Chapter 1: Machine Learning Project Life Cycle and Challenges FREE CHAPTER

3. Chapter 2: What Is MLOps, and Why Is It So Important for Every ML Team?

4. Part 2: Machine Learning Tools for Custom Models on Google Cloud

5. Chapter 3: It’s All About Data – Options to Store and Transform ML Datasets

6. Chapter 4: Vertex AI Workbench – a One-Stop Tool for AI/ML Development Needs

7. Chapter 5: No-Code Options for Building ML Models

8. Chapter 6: Low-Code Options for Building ML Models

9. Chapter 7: Training Fully Custom ML Models with Vertex AI

10. Chapter 8: ML Model Explainability

11. Chapter 9: Model Optimizations – Hyperparameter Tuning and NAS

12. Chapter 10: Vertex AI Deployment and Automation Tools – Orchestration through Managed Kubeflow Pipelines

13. Chapter 11: MLOps Governance with Vertex AI

14. Part 3: Prebuilt/Turnkey ML Solutions Available in GCP

15. Chapter 12: Vertex AI – Generative AI Tools

16. Chapter 13: Document AI – An End-to-End Solution for Processing Documents

17. Chapter 14: ML APIs for Vision, NLP, and Speech

18. Part 4: Building Real-World ML Solutions with Google Cloud

19. Chapter 15: Recommender Systems – Predict What Movies a User Would Like to Watch

20. Chapter 16: Vision-Based Defect Detection System – Machines Can See Now!

21. Chapter 17: Natural Language Models – Detecting Fake News Articles!

22. Index

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23. Other Books You May Enjoy

Getting started with BigQuery

Google BigQuery is a serverless, fully managed data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. Since BigQuery is not part of Vertex AI, we won’t be covering the features of the tool in depth in this book, but here’s a quick guide on getting started. This should be enough information to help you follow along with the exercises later in this chapter:

Set up a Google Cloud project: Before you can use BigQuery, you’ll need to set up a Google Cloud Platform (GCP) project. Head over to the Google Cloud console and create a new project. If you’ve never used GCP before, you might need to create an account and set up billing information.
Enable the BigQuery API: Within your GCP project, navigate to the API & Services section and enable the BigQuery API.
Access the BigQuery console: Once the API has been enabled, you can access the BigQuery console either...

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You're reading from The Definitive Guide to Google Vertex AI Accelerate your machine learning journey with Google Cloud Vertex AI and MLOps best practices

Table of Contents (24) Chapters

Getting started with BigQuery

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Authors (2)

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