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The Machine Learning Solutions Architect Handbook

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

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781805122500
Length 602 pages
Edition 2nd Edition
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Author (1):
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David Ping David Ping
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David Ping
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Table of Contents (19) Chapters Close

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

In this chapter, we provided a comprehensive overview of the generative AI project lifecycle, from identifying business use cases to model deployment. We explored major generative technologies like FMs and key techniques for customization including domain adaptation, instruction tuning, reinforcement learning with human feedback, and prompt engineering.

The chapter also covered specialized engineering considerations around large model hosting and mitigating risks like factual inaccuracies. While limitations exist, responsible development and governance can allow enterprises across industries to harness generative AI’s immense potential for creating business value. With an understanding of the end-to-end lifecycle, practitioners can thoughtfully architect and deliver innovative yet practical generative AI solutions.

In the next chapter, we will talk about the key considerations for building a generative AI platform, retrieval-augmented generation (RAG) solutions...

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