A practical approach to implementing MLOps with Red Hat OpenShift. See how Red Hat and its technology partners such as Pachyderm and Intel OpenVINO provide a consistent platform across clouds and on-premises hardware.

Key FeaturesUnderstand the concepts of MLOps and machine learning project lifecycleExperience provisioning an MLOps ecosystem using a step-by-step guideBuild a complete ML workflow from using OpenShift Data ScienceBook DescriptionMLOps with OpenShift provides a practical approach to implementing MLOps workflows on the OpenShift platform. The book begins by introducing key MLOps concepts, including data preparation, model training, and deployment.

It then provides an overview of OpenShift, covering the basic blocks of OpenShift, such as containers, pods and operators. With the basics covered, the book then dives into MLOps workflows on the OpenShift platform.

Readers will learn how popular machine learning frameworks are used to train and test models on the platform. The book will cover Red Hat OpenShift Data Science, an open-source data science and machine learning platform designed to run on the OpenShift platform.

Red Hat OpenShift Data Science and partner components provide the building blocks to build and manage data pipelines and deploy and monitor machine learning models. Pachyderm and Intel OpenVINO are two such partner components covered in the book.

By the end of the book, readers will have a solid understanding of MLOps concepts and best practices, as well as the skills and knowledge needed to implement MLOps workflows on the OpenShift platform. What you will learnGain a deep understanding of the key concepts and best practices of MLOpsLearn different components that cover the essential MLOps workflows for developing, training, testing, deploying, and monitoring machine learning models at scaleImplement complete MLOps workflows on the Red Hat OpenShift platformConfigure different components of an MLOps platform on an OpenShift clusterLearn how different teams can collaborate over the OpenShift platformLearn how to operate data science and machine learning workloads in OpenShiftWho This Book Is ForMLOps and DevOps engineers, Data architects and scientists, and developers who want to learn MLOps and its components are the primary audiences.

They will learn how to integrate these components for building a complete machine learning lifecycle on the Red Hat OpenShift Data Science platform. Whether you are a data scientist, machine learning engineer, or software developer, "MLOps with OpenShift" is the essential resource for building scalable and efficient machine learning workflows on the OpenShift container platform.

Table of ContentsIntroduction to MLOps and OpenShiftThe Machine Learning LifecycleProvisioning an MLOps platform in the CloudBuilding Machine Learning ModelsEmbedding ML Models into the ApplicationsDeploying ML Models as a ServiceOperating ML workloadsBuilding MLOps platform for success. .