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Machine Learning on Kubernetes
This is the code repository for Machine Learning on Kubernetes, published by Packt.
A practical handbook for building and using a complete open source machine learning platform on Kubernetes
What is this book about?
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
<img src="https://raw.githubusercontent.com/PacktPublishing/GitHub/master/GitHub.png" alt="https://www.packtpub.com/" border="5" />
Instructions and Navigations
All of the code is organized into folders.
The code will look like the following:
docker tag scikit-notebook:v1.1.0 quay.io/ml-on-k8s/scikitnotebook:v1.1.0Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
Software and Hardware List
| Software required | OS required |
|---|---|
| kubernetes, Python, Spark, MLflow, | Windows, Mac OS X, and Linux (Any) |
| Seldon, Airflow |
Running the platform requires a good amount of compute resources. If you do not have the required number of CPU cores and memory on your desktop or laptop computer, we recommend running a virtual machine on Google Cloud or any other cloud platform
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Errata and Troubleshooting tips
- Chapter 10: If you encounter the following error in chapter 10, "seldon_core.wrapper:handle_invalid_usage:60 - ERROR: {'status': {'status': 1, 'info': 'Invalid request data type", try using the following data.json file. More details regarding this error can be found here in this issue thread, courtesy of our readers @aquynh1682 and @webmakaka.
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Get to Know the Authors
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.
Download a free PDF
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.