You're reading from MLOps with Red Hat OpenShift

Product typeBook

Published inJan 2024

PublisherPackt

ISBN-139781805120230

Edition1st Edition

Concepts

Machine Learning

Authors (2):

Ross Brigoli

Faisal Masood

View More author details

Summary

In this chapter, you have seen how Red Hat ODS integrates with third-party software to further simplify your MLOps journey. OpenShift makes it a breeze to use the data versioning capabilities of the Pachyderm software.

You have seen how Red Hat OpenShift Pipelines enables your data science team to automate the model training workflow by providing drag-and-drop capabilities and using the same code you have used to manually train the model.

You will use the core pipeline capabilities in the next few chapters, where you'll further automate your workflow. In the next chapter, you will use what you have learned about creating pipelines to deploy the model as a service.

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MLOps with Red Hat OpenShift

Published in: Jan 2024Publisher: PacktISBN-13: 9781805120230

Authors (2)

Ross Brigoli

Ross Brigoli is a consulting architect at Red Hat, where he focuses on designing and delivering solutions around microservices architecture, DevOps, and MLOps with Red Hat OpenShift for various industries. He has two decades of experience in software development and architecture.
Read more about Ross Brigoli

Faisal Masood

Faisal Masood is a cloud transformation architect at AWS. Faisal's focus is to assist customers in refining and executing strategic business goals. Faisal main interests are evolutionary architectures, software development, ML lifecycle, CD and IaC. Faisal has over two decades of experience in software architecture and development.
Read more about Faisal Masood

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