You're reading from Google Machine Learning and Generative AI for Solutions Architects Build efficient and scalable AI/ML solutions on Google Cloud

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781803245270

Length 552 pages

Edition 1st Edition

Languages

Gen

Tools

Google Cloud Platform

Concepts

Machine Learning

Author (1):

Kieran Kavanagh

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Table of Contents (24) Chapters

Preface

1. Part 1:The Basics FREE CHAPTER

2. Chapter 1: AI/ML Concepts, Real-World Applications, and Challenges

3. Chapter 2: Understanding the ML Model Development Life Cycle

4. Chapter 3: AI/ML Tooling and the Google Cloud AI/ML Landscape

5. Part 2:Diving in and building AI/ML solutions

6. Chapter 4: Utilizing Google Cloud’s High-Level AI Services

7. Chapter 5: Building Custom ML Models on Google Cloud

8. Chapter 6: Diving Deeper – Preparing and Processing Data for AI/ML Workloads on Google Cloud

9. Chapter 7: Feature Engineering and Dimensionality Reduction

10. Chapter 8: Hyperparameters and Optimization

11. Chapter 9: Neural Networks and Deep Learning

12. Chapter 10: Deploying, Monitoring, and Scaling in Production

13. Chapter 11: Machine Learning Engineering and MLOps with Google Cloud

14. Chapter 12: Bias, Explainability, Fairness, and Lineage

15. Chapter 13: ML Governance and the Google Cloud Architecture Framework

16. Chapter 14: Additional AI/ML Tools, Frameworks, and Considerations

17. Part 3:Generative AI

18. Chapter 15: Introduction to Generative AI

19. Chapter 16: Advanced Generative AI Concepts and Use Cases

20. Chapter 17: Generative AI on Google Cloud

21. Chapter 18: Bringing It All Together: Building ML Solutions with Google Cloud and Vertex AI

22. Index

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Additional AI/ML Tools, Frameworks, and Considerations

At this point, we have covered all of the major steps and considerations in a typical machine learning (ML) project. Considering that AI/ML is one of the fastest-developing areas of research in the technology industry, new tools, methodologies, and frameworks emerge every day.

In this chapter, we will discuss additional tools and frameworks that are popular in the data science industry that we haven’t covered so far. This includes important topics such as BigQuery ML (BQML), various types of hardware that we can use for AI/ML workloads, and the use of open source libraries and frameworks such as PyTorch, Ray, and Spark MLlib. We will also discuss some tips on how to implement large-scale distributed training on Google Cloud.

At the end of this chapter, I will provide some additional context to help transition the focus of the remainder of this book to Generative AI. This will include diving a bit deeper into some of...