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MLOps with Red Hat OpenShift

You're reading from   MLOps with Red Hat OpenShift A cloud-native approach to machine learning operations

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781805120230
Length 238 pages
Edition 1st Edition
Tools
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Authors (2):
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Ross Brigoli Ross Brigoli
Author Profile Icon Ross Brigoli
Ross Brigoli
Faisal Masood Faisal Masood
Author Profile Icon Faisal Masood
Faisal Masood
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Toc

Table of Contents (13) Chapters Close

Preface 1. Part 1: Introduction FREE CHAPTER
2. Chapter 1: Introduction to MLOps and OpenShift 3. Part 2: Provisioning and Configuration
4. Chapter 2: Provisioning an MLOps Platform in the Cloud 5. Chapter 3: Building Machine Learning Models with OpenShift 6. Part 3: Operating ML Workloads
7. Chapter 4: Managing a Model Training Workflow 8. Chapter 5: Deploying ML Models as a Service 9. Chapter 6: Operating ML Workloads 10. Chapter 7: Building a Face Detector Using the Red Hat ML Platform 11. Index 12. Other Books You May Enjoy

Installing Red Hat ODS

The Red Hat ODS service enables data science teams to execute data science and ML workflows by integrating Red Hat components, open source software (OSS), and partner offerings. Its primary goal is to provide a collaborative and scalable environment for data scientists and data engineers to develop, deploy, and manage ML and AI applications.

With ODS, data scientists can leverage familiar tools such as Jupyter Notebook to create interactive development environments for their data analysis and model development tasks. You can easily build and train ML models using frameworks such as TensorFlow, PyTorch, and scikit-learn. Red Hat ODS includes JupyterHub, Git integration, model deployment, and model serving and is based on the upstream open source project, Open Data Hub (ODH). You will learn more about the details and internal components of Red Hat ODS in the next chapters.

OpenShift makes it very easy to install ODS in the following easy steps. ODS is packaged...

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