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

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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

What industries are doing with generative AI

Enterprises across diverse sectors are actively engaging in the exploration of potential applications for generative AI technology, even though it is still early days in the adoption of generative AI. These enterprises are looking into this innovative technology to drive tangible business outcomes including increased productivity, enhanced customer experiences, novel business insights, and the creation of new products and services. With all the excitement surrounding this technology, it is also important to understand what’s practical and what is aspirational. With that in mind, let’s delve into some active areas of exploration of the adoption of generative AI.

Financial services

As leaders in technology adoption, financial services firms are actively exploring generative AI use cases across banking, capital markets, insurance, and financial data.

The majority of current generative AI applications focus on document...