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