MLOps with Red Hat OpenShift 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.
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 learnBuild a solid foundation in key MLOps concepts and best practicesExplore MLOps workflows, covering model development and trainingImplement complete MLOps workflows on the Red Hat OpenShift platformBuild MLOps pipelines for automating model training and deploymentsDiscover model serving approaches using Seldon and Intel OpenVinoGet to grips with operating data science and machine learning workloads in OpenShiftWho 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 ContentsIntroduction to MLOps and OpenShiftProvisioning an MLOps platform in the CloudBuilding Machine Learning ModelsEmbedding ML Models into the ApplicationsDeploying ML Models as a ServiceOperating ML workloadsBuilding a face detector using the Red Hat ML Platform


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