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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

Chapter 4: Data Management for Machine Learning

As a practitioner of machine learning (ML) solutions architecture, I often get asked to help provide architecture advice on data management platforms for ML workloads. While data management platform architecture is mainly considered a separate technical discipline, it is an integral part of ML workloads. To design a comprehensive ML platform, ML solutions architects need to be familiar with the key data architecture considerations for machine learning and know the technical design of a data management platform to meet the needs of data scientists and the automated ML pipelines. In this chapter, we will look at where data management intercepts with ML. We will talk about key considerations for designing a data management platform for ML. We will then deep dive into the core architecture components for a data management platform and the relevant AWS technologies and services for building a data management platform on AWS. Finally, you will...

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