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

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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 6: Kubernetes Container Orchestration Infrastructure Management

While it is fairly straightforward to build a local data science environment with open source technologies for individual uses in simple machine learning (ML) tasks, it is quite challenging to configure and maintain a data science environment for many users for different ML tasks and track ML experiments. Building an end-to-end ML platform is a complex process, and there are many different architecture patterns and open source technologies available to help. In this chapter, we will cover Kubernetes, an open source container orchestration platform that can serve as the foundational infrastructure for building open source ML platforms. We will discuss the core concept of Kubernetes, its networking architecture and components, and its security and access control. You will also get hands-on with Kubernetes to build a Kubernetes cluster and use it to deploy containerized applications.

Specifically, we will cover...