MLOps with Red Hat OpenShift (eBook)

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.

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Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflowsKey FeaturesGrasp MLOps and machine learning project lifecycle through concept introductionsGet hands on with provisioning and configuring Red Hat OpenShift Data ScienceExplore model training, deployment, and MLOps pipeline building with step-by-step instructionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMLOps 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 forThis 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.]]>