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The Machine Learning Solutions Architect Handbook

You're reading from   The Machine Learning Solutions Architect Handbook Create machine learning platforms to run solutions in an enterprise setting

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072168
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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David Ping David Ping
Author Profile Icon David Ping
David Ping
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Solving Business Challenges with Machine Learning Solution Architecture
2. Chapter 1: Machine Learning and Machine Learning Solutions Architecture FREE CHAPTER 3. Chapter 2: Business Use Cases for Machine Learning 4. Section 2: The Science, Tools, and Infrastructure Platform for Machine Learning
5. Chapter 3: Machine Learning Algorithms 6. Chapter 4: Data Management for Machine Learning 7. Chapter 5: Open Source Machine Learning Libraries 8. Chapter 6: Kubernetes Container Orchestration Infrastructure Management 9. Section 3: Technical Architecture Design and Regulatory Considerations for Enterprise ML Platforms
10. Chapter 7: Open Source Machine Learning Platforms 11. Chapter 8: Building a Data Science Environment Using AWS ML Services 12. Chapter 9: Building an Enterprise ML Architecture with AWS ML Services 13. Chapter 10: Advanced ML Engineering 14. Chapter 11: ML Governance, Bias, Explainability, and Privacy 15. Chapter 12: Building ML Solutions with AWS AI Services 16. Other Books You May Enjoy

Summary

In this chapter, we covered several topics, including what AI and ML are, the key steps in an end-to-end ML life cycle, and the core functions of ML solutions architecture. Now, you should be able to identify the key differences between the three main types of ML and the kind of business problems they can solve. You have also learned that business and data understanding is critical to the successful outcome of an ML project, in addition to modeling and engineering. Lastly, you now have an understanding of how ML solutions architecture fits into the ML life cycle.

In the next chapter, we will go over some ML use cases across a number of industries, such as financial services and media and entertainment.

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The Machine Learning Solutions Architect Handbook
Published in: Jan 2022
Publisher: Packt
ISBN-13: 9781801072168
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