Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Build a Kubernetes-based self-serving, agile data science and machine learning ecosystem for your organization using reliable and secure open source technologies

Key Features

• Build a complete machine learning platform on Kubernetes
• Improve the agility and velocity of your team by adopting the self-service capabilities of the platform
• Reduce time-to-market by automating data pipelines and model training and deployment

Book Description

MLOps is an emerging field that aims to bring repeatability, automation, and standardization of the software engineering domain to data science and machine learning engineering. By implementing MLOps with Kubernetes, data scientists, IT professionals, and data engineers can collaborate and build machine learning solutions that deliver business value for their organization.

You'll begin by understanding the different components of a machine learning project. Then, you'll design and build a practical end-to-end machine learning project using open source software. As you progress, you'll understand the basics of MLOps and the value it can bring to machine learning projects. You will also gain experience in building, configuring, and using an open source, containerized machine learning platform. In later chapters, you will prepare data, build and deploy machine learning models, and automate workflow tasks using the same platform. Finally, the exercises in this book will help you get hands-on experience in Kubernetes and open source tools, such as JupyterHub, MLflow, and Airflow.

By the end of this book, you'll have learned how to effectively build, train, and deploy a machine learning model using the machine learning platform you built.

What you will learn

• Understand the different stages of a machine learning project
• Use open source software to build a machine learning platform on Kubernetes
• Implement a complete ML project using the machine learning platform presented in this book
• Improve on your organization's collaborative journey toward machine learning
• Discover how to use the platform as a data engineer, ML engineer, or data scientist
• Find out how to apply machine learning to solve real business problems

Who this book is for

This book is for data scientists, data engineers, IT platform owners, AI product owners, and data architects who want to build their own platform for ML development. Although this book starts with the basics, a solid understanding of Python and Kubernetes, along with knowledge of the basic concepts of data science and data engineering will help you grasp the topics covered in this book in a better way.

Table of Contents

1. Challenges in Machine Learning
2. Understanding MLOps
3. Exploring Kubernetes
4. The Anatomy of a Machine Learning Platform
5. Data Engineering
6. Machine Learning Engineering
7. Model Deployment and Automation
8. Building a Complete ML Project Using the Platform
9. Building Your Data Pipeline
10. Building, Deploying and Monitoring Your Model
11. Machine Learning on Kubernetes

Faisal Masood is a principal architect at Red Hat. He has been helping teams to design and build data science and application platforms using OpenShift, Red Hat’s enterprise Kubernetes offering. Faisal has over 20 years of experience in building software and has been building microservices since the pre-Kubernetes era.

Ross Brigoli is an associate principal architect at Red Hat. He has been designing and building software in various industries for over 18 years. He has designed and built data platforms and workflow automation platforms. Before Red Hat, Ross led a data engineering team as an architect in the financial services industry. He currently designs and builds microservices architectures and machine learning solutions on OpenShift.