This is the code repository for MLOps with Red Hat OpenShift, published by Packt.
A cloud-native approach to machine learning operations
MLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more.
This book covers the following exciting features:
- Build a solid foundation in key MLOps concepts and best practices
- Explore MLOps workflows, covering model development and training
- Implement complete MLOps workflows on the Red Hat OpenShift platform
- Build MLOps pipelines for automating model training and deployments
- Discover model serving approaches using Seldon and Intel OpenVino
- Get to grips with operating data science and machine learning workloads in OpenShift
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
storage:
backend: MINIO
minio:
bucket: pachyderm
Following is what you need for this book: This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| 1-7 | AWS Web Services(AWS) with a recent version of a modern web browser(Chrome, Edge, etc.) | Any OS |
| 1-7 | Red Hat OpenShift Client (oc) | Any OS |
Ross Brigoli is an associate principal architect at Red Hat. He boasts an impressive career of over two decades, marked by profound expertise in software engineering, solution design, and software architecture. Ross, along with Faisal, co-authored the book “Machine Learning on Kubernetes”.
Faisal Masood is a cloud transformation architect at AWS. Faisal’s focus is to assist customers in refining and executing strategic business goals. Faisal main interests are evolutionary architectures, software development, ML lifecycle, CD and IaC. Faisal has over two decades of experience in software architecture and development.