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

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072168

Length 442 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

David Ping

View More author details

Table of Contents (17) Chapters

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...