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 exercise – building a data science architecture using open source technologies

In this hands-on exercise, you will build an ML platform using several open source ML platform software. There are three main parts to this hands-on exercise:

Installing Kubeflow and setting up a Kubeflow notebook
Tracking experiments, managing models, and deploying models
Automating the ML steps with Kubeflow Pipelines

Alright, let's get started with the first part – installing Kubeflow on the Amazon EKS cluster.

Part 1 – Installing Kubeflow

You will continue to use the Amazon (EKS) infrastructure you created earlier and install Kubeflow on top of it. To start, let's complete the following steps:

Launch AWS CloudShell: Log in to your AWS account, select the Oregon region, and launch AWS CloudShell again.
Install the kfctl utility: The kfctl utility is a command-line utility for installing and managing Kubeflow. Run the following...