MLOps with Red Hat OpenShift: A cloud-native approach to machine learning operations

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.

使用Red Hat OpenShift Data Science建立和管理MLOps流程的實用指南，釋放機器學習工作流程的威力。

主要特點：
- 通過概念介紹掌握MLOps和機器學習項目生命周期
- 透過實際操作來配置和設置Red Hat OpenShift Data Science
- 逐步指導探索模型訓練、部署和MLOps流程構建
- 購買印刷版或Kindle電子書可獲得免費PDF電子書

書籍描述：
《MLOps with OpenShift》提供了在動態OpenShift平台上實施MLOps工作流程的實用見解。隨著全球組織尋求利用機器學習運營的力量，本書為您的MLOps成功奠定了基礎。從探索關鍵的MLOps概念開始，包括數據準備、模型訓練和部署，您將準備好發揮OpenShift的能力，從容器、Pod、運算元等基礎知識開始。

在基礎知識奠定的基礎上，您將被引導進入MLOps工作流程，揭示在平台上使用流行的機器學習框架進行模型訓練和測試的應用。

隨著您在各章節中的進展，您將專注於開源數據科學和機器學習平台Red Hat OpenShift Data Science及其合作夥伴組件，如Pachyderm和Intel OpenVino，以了解它們在構建和管理數據流程以及部署和監控機器學習模型中的角色。

憑藉這份全面的知識，您將能夠在OpenShift平台上熟練實施MLOps工作流程。

學到什麼：
- 在關鍵的MLOps概念和最佳實踐方面建立堅實基礎
- 探索MLOps工作流程，包括模型開發和訓練
- 在Red Hat OpenShift平台上實施完整的MLOps工作流程
- 構建自動化模型訓練和部署的MLOps流程
- 使用Seldon和Intel OpenVino發現模型服務方法
- 熟悉在OpenShift中操作數據科學和機器學習工作負載

本書適合對OpenShift平台感興趣的MLOps和DevOps工程師、數據架構師和數據科學家。特別是那些想要學習MLOps及其組件的開發人員會發現本書很有用。無論您是機器學習工程師還是軟件開發人員，本書都是在OpenShift平台上構建可擴展和高效的機器學習工作流程的必備指南。