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MLOps with Red Hat OpenShift

You're reading from  MLOps with Red Hat OpenShift

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Pages 238 pages
Edition 1st Edition
Languages
Authors (2):
Ross Brigoli Ross Brigoli
Profile icon Ross Brigoli
Faisal Masood Faisal Masood
Profile icon Faisal Masood
View More author details

Table of Contents (13) Chapters

Preface Part 1: Introduction
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 Other Books You May Enjoy

Summary

Congratulations! You have just experienced building an end-to-end MLOps workflow from scratch. You have trained and deployed an ML model and built a pipeline to automate your model training and deployment workflow using the tools that come with OpenShift Data Science. You have also successfully built a backend application that hosts your model and exposes it as an HTTP endpoint.

You have seen how OpenShift not only provides a full ML life cycle but also hosts your application and supports technologies such as Redis. All the components that have been deployed will benefit from the scalability of the platform.

Your journey does not stop here. The models we have shown here are just an example. You can deploy open source large language models (LLMs) on the platform.

Happy learning!

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