Faisal Masood - Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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- Book:Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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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
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.
Youll begin by understanding the different components of a machine learning project. Then, youll design and build a practical end-to-end machine learning project using open source software. As you progress, youll 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, youll 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 organizations 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
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- Challenges in Machine Learning
- Understanding MLOps
- Exploring Kubernetes
- The Anatomy of a Machine Learning Platform
- Data Engineering
- Machine Learning Engineering
- Model Deployment and Automation
- Building a Complete ML Project Using the Platform
- Building Your Data Pipeline
- Building, Deploying and Monitoring Your Model
- Machine Learning on Kubernetes
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A practical handbook for building and using a complete open source machine learning platform on Kubernetes
Faisal Masood
Ross Brigoli
BIRMINGHAMMUMBAI
Copyright 2022 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Publishing Product Manager: Dhruv Jagdish Kataria
Senior Editor: David Sugarman
Content Development Editor: Priyanka Soam
Technical Editor: Devanshi Ayare
Copy Editor: Safis Editing
Project Coordinator: Farheen Fathima
Proofreader: Safis Editing
Indexer: Manju Arasan
Production Designer: Nilesh Mohite
Marketing Coordinators: Shifa Ansari, Abeer Riyaz Dawe
First published: June 2022
Production reference: 1190522
Published by Packt Publishing Ltd.
Livery Place
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Birmingham
B3 2PB, UK.
ISBN 978-1-80324-180-7
www.packt.com
"To my daughter, Yleana Zorelle hopefully, this book will help you understand what Papa does for a living."
Ross Brigoli
"To my wife, Bushra Arif without your support, none of this would have become a reality"
Faisal Masood
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.
Audrey Reznik is a senior principal software engineer in the Red Hat Cloud Services OpenShift Data Science team focusing on managed services, AI/ML workloads, and next-generation platforms. She has been working in the IT Industry for over 20 years in full stack development relating to data science roles. As a former technical advisor and data scientist, Audrey has been instrumental in educating data scientists and developers about what the OpenShift platform is and how to use OpenShift containers (images) to organize, develop, train, and deploy intelligent applications using MLOps. She is passionate about data science and, in particular, the current opportunities with machine learning and open source technologies.
Cory Latschkowski has made a number of major stops in various IT fields over the past two decades, including high-performance computing (HPC), cybersecurity, data science, and container platform design. Much of his experience was acquired within large organizations, including one Fortune 100 company. His last name is pronounced Latch - cow - ski. His passions are pretty moderate, but he will admit to a love of automation, Kubernetes, RTFM, and bacon. To learn more about his personal bank security questions, ping him on GitHub.
Shahebaz Sayed is a highly skilled certified cloud computing engineer with exceptional development ability and extensive knowledge of scripting and data serialization languages. Shahebaz has expertise in all three major clouds AWS, Azure, and GCP. He also has extensive experience with technologies such as Kubernetes, Terraform, Docker, and others from the DevOps domain. Shahebaz is also certified with global certifications, including AWS Certified DevOps Engineer Professional, AWS Solution Architect Associate, Azure DevOps Expert, Azure Developer Associate, and Kubernetes CKA. He has also worked with Packt as a technical reviewer on multiple projects, including AWS Automation Cookbook, Kubernetes on AWS, and Kubernetes for Serverless Applications.
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