What this book covers
Chapter 1, Introduction to MLOps and OpenShift, starts with a brief introduction to MLOps and the basics of Red Hat OpenShift. The chapter then discusses how OpenShift enables machine learning projects and how Red Hat OpenShift Data Science and partner software products comprise a complete MLOPS platform.
Chapter 2, Provisioning an MLOps Platform in the Cloud, will walk you through provisioning Red Hat OpenShift, Red Hat OpenShift Data Science, and Pachyderm on the AWS cloud. The chapter contains step-by-step instructions on how to provision the base MLOps platform.
Chapter 3, Building Machine Learning Models with OpenShift, starts with the initial configurations of the platform components to prepare for model building. The chapter walks you through the configuration steps and ends with an introduction to the data science projects, workbenches, and the Jupyter Notebook.
Chapter 4, Managing a Model Training Workflow, digs deeper into the platform configuration covering OpenShift Pipelines for building model training pipelines and using Pachyderm for data versioning. By the end of the chapter, you will have built an ML model using a training pipeline you created.
Chapter 5, Deploying ML Models as a Service, introduces the model serving component of the platform. The chapter will walk you through how to enhance further the pipeline to automate the deployment of ML models.
Chapter 6, Operating ML Workloads, talks about the operational aspects of MLOps. The chapter focuses on logging and monitoring the deployed ML models and briefly discusses strategies for optimizing operational costs.
Chapter 7, Building a Face Detector Using the Red Hat ML Platform, walks you through the process of building a new AI-enabled application from end to end. The chapter helps you practice the knowledge and skills you gained in the previous chapters. The chapter also introduces Intel OpenVino as another option for model serving. By the end of this chapter, you will have built an AI-enabled web application running on OpenShift and used all of the Red Hat OpenShift Data Science features.