You're reading from The Machine Learning Solutions Architect Handbook Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781805122500

Length 602 pages

Edition 2nd Edition

Languages

Python

Tools

MLOps

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (19) Chapters

Preface

1. Navigating the ML Lifecycle with ML Solutions Architecture FREE CHAPTER

2. Exploring ML Business Use Cases

3. Exploring ML Algorithms

4. Data Management for ML

5. Exploring Open-Source ML Libraries

6. Kubernetes Container Orchestration Infrastructure Management

7. Open-Source ML Platforms

8. Building a Data Science Environment Using AWS ML Services

9. Designing an Enterprise ML Architecture with AWS ML Services

10. Advanced ML Engineering

11. Building ML Solutions with AWS AI Services

12. AI Risk Management

13. Bias, Explainability, Privacy, and Adversarial Attacks

14. Charting the Course of Your ML Journey

15. Navigating the Generative AI Project Lifecycle

16. Designing Generative AI Platforms and Solutions

17. Other Books You May Enjoy

18. Index

Summary

Throughout this chapter, we have explored various industries and the ways in which they are utilizing ML to solve business challenges and drive growth. From finance and healthcare to retail and automotive, we have seen how ML can improve processes, generate insights, and enhance the customer experience. The examples within this chapter have hopefully sparked ideas you can now bring to stakeholders to kickstart an ML roadmap discussion and think creatively about the potential high-impact applications in your own organizations.

As we move into the next chapter, we will delve deeper into the mechanics of ML, exploring the fundamental concepts behind how machines learn and some of the most widely used algorithms in the field. This will provide you with a solid foundation for understanding how ML is applied in practice to solve various ML problems.