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MLOps with Red Hat OpenShift
By :
MLOps with Red Hat OpenShift
By:
Overview of this book
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.
Table of Contents (13 chapters)
Preface
Chapter 1: Introduction to MLOps and OpenShift
Part 2: Provisioning and Configuration
Chapter 2: Provisioning an MLOps Platform in the Cloud
Chapter 3: Building Machine Learning Models with OpenShift
Part 3: Operating ML Workloads
Chapter 4: Managing a Model Training Workflow
Chapter 5: Deploying ML Models as a Service
Chapter 6: Operating ML Workloads
Chapter 7: Building a Face Detector Using the Red Hat ML Platform
Index
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