Product Documentation for Red Hat OpenShift AI Self-Managed 2.16

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Features, enhancements, resolved issues, and known issues associated with this release

OpenShift AI is a platform for data scientists and developers of artificial intelligence and machine learning (AI/ML) applications

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Learn how to work in an OpenShift AI environment

Use OpenShift AI to train an example model in JupyterLab, deploy the model, and refine the model by using automated pipelines

Work with data stored in an S3-compatible object store from your workbench

Organize your work in projects and workbenches, create and collaborate on notebooks, train and deploy models, configure model servers, and implement pipelines

Working in your data science IDE from Red Hat OpenShift AI Self-Managed

Work with data science pipelines from Red Hat OpenShift AI Self-Managed

Monitor your OpenShift AI models for fairness

Use distributed workloads for faster and more efficient data processing and model training

Connect to applications from Red Hat OpenShift AI Self-Managed

Working with model registries in Red Hat OpenShift AI Self-Managed

Serve models in Red Hat OpenShift AI Self-Managed

View a list of API tiers and API version examples for OpenShift AI

Manage administration tasks from the OpenShift AI dashboard

Working with accelerators from Red Hat OpenShift AI Self-Managed

Managing model registries in Red Hat OpenShift AI Self-Managed

Supported software platforms and architecture

Install and uninstall OpenShift AI Self-Managed

Install and uninstall OpenShift AI Self-Managed in a disconnected environment

Upgrade OpenShift AI on OpenShift

Upgrade Red Hat OpenShift AI on OpenShift in a disconnected environment

Cluster administrator tasks for managing OpenShift AI

Configuring the model registry component in Red Hat OpenShift AI Self-Managed