Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows
Key Features
- Grasp MLOps and machine learning project lifecycle through concept introductions
- Get hands on with provisioning and configuring Red Hat OpenShift Data Science
- Explore model training, deployment, and MLOps pipeline building with step-by-step instructions
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
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.
With the groundwork in place, you'll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform.
As you advance through the chapters, you'll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models.
Armed with this comprehensive knowledge, you'll be able to implement MLOps workflows on the OpenShift platform proficiently.
What you will learn
- 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
Who this book is for
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.
Table of Contents
- Introduction to MLOps and OpenShift
- Provisioning an MLOps platform in the Cloud
- Building Machine Learning Models
- Embedding ML Models into the Applications
- Deploying ML Models as a Service
- Operating ML workloads
- Building a face detector using the Red Hat ML Platform
Ross Brigoli is a consulting architect at Red Hat, where he focuses on designing and delivering solutions around microservices architecture, DevOps, and MLOps with Red Hat OpenShift for various industries. He has two decades of experience in software development and architecture.
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.
