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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Background information – libraries

Before we dive in and start building our own models, let’s take a moment to discuss some of the software libraries we will use in this chapter.

Definition

A software library is a collection of code and data that includes useful functions and tools for specific types of programming tasks. When common types of programming tasks are identified in a given industry, such as data manipulation or implementing complex mathematical equations, then usually somebody will eventually create a library that contains the code and other resources required to perform those tasks. The library can then easily be used by others to achieve those same tasks and potentially extended to add more functionality over time, rather than everybody needing to write the code to perform those common tasks over and over again. Without libraries, programmers would have to build everything from scratch all of the time and would waste a lot of time on rudimentary programming...