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

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Hands-on – creating a Kubernetes infrastructure on AWS

In this section, you will create a Kubernetes environment using the Amazon EKS. Let's first look at the problem statement in the following section.

Problem statement

As a ML solutions architect, you have been tasked to evaluate Kubernetes as a potential infrastructure platform for building an ML platform for one business unit in your bank. You need to build a sandbox environment on AWS and demonstrate that you can deploy a Jupyter Notebook as a containerized application for your data scientists to use.

Lab instruction

In this hands-on exercise, you are going to create a Kubernetes environment using the Amazon EKS. The EKS is a managed service for Kubernetes on AWS that creates and configures a Kubernetes cluster with both master and worker nodes automatically. The EKS provisions and scales the control plane, including the API server and backend persistent layer. The EKS runs the open source Kubernetes and...