AI/ML: Persistent workspaces for multiple users

The first challenge for an AI/ML practitioner is gathering the necessary data to feed the process. The solution? Advanced planning algorithms that organize data better than humans in far less time.

AI/ML Resources

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Article

Open Data Hub 1.1.0 provides new JupyterHub capabilities and more

July 26, 2021

Explore new features in Open Data Hub 1.1.0, including the JupyterHub Spawner UI, Kubeflow 1.3, Red Hat OpenShift Pipelines, and Trino SQL query engine.

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Article

How to apply machine learning to GitOps

June 29, 2021

Explore how you can apply machine learning in each phase of the GitOps life cycle to improve package building, testing, deploying, monitoring, and security scanning.

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Article

Modern Fortune Teller: Using GitOps to automate application deployment on Red Hat OpenShift

June 21, 2021

Find out how the Modern Fortune Teller team used Open Data Hub and GitOps to develop a machine learning application automated for continuous deployment on Red Hat OpenShift.

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Article

How to implement employee rostering with Red Hat Business Optimizer

June 9, 2021

Move through a sample use case for employee shift roster scheduling that accounts for multiple planning variables using Red Hat Business Optimizer.

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Article

How to install Kubeflow 1.2 on Red Hat OpenShift

May 28, 2021

Kubeflow is a deployment tool designed specifically for machine learning applications. In this article, you'll learn how to install Kubeflow on Red Hat OpenShift using the Open Data Hub Operator.

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Article

Can we consider --editable a bad practice?

May 26, 2021

Editable installs make sense in certain contexts, but should be considered a bad practice for data scientists using Project Thoth. Find out how --editable breaks Project Thoth's built-in dependency management features, and why you shouldn't use it.

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