Red Hat SummitRed Hat OpenShift AI Self-Managed 2.23
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Welcome
Release notes
Features, enhancements, resolved issues, and known issues associated with this release
Introduction to Red Hat OpenShift AI
OpenShift AI is a platform for data scientists and developers of artificial intelligence and machine learning (AI/ML) applications
Supported configurations
Supported software platforms and architecture
Product life cycle
Understand the product life cycle to plan deployments and support applications using the product
Red Hat AI learning hub
Explore a curated collection of learning resources designed to help you accomplish key tasks with Red Hat AI products and services
Provide feedback on Red Hat documentation
Let Red Hat know how we can make our documentation better
Documentation for OpenShift cluster administrators
Guidance on installing and managing OpenShift AI for OpenShift cluster administrators
Installing and uninstalling OpenShift AI Self-Managed
Install and uninstall OpenShift AI Self-Managed
Installing and uninstalling OpenShift AI Self-Managed in a disconnected environment
Install and uninstall OpenShift AI Self-Managed in a disconnected environment
Upgrading OpenShift AI Self-Managed
Upgrade OpenShift AI on OpenShift
Upgrading OpenShift AI Self-Managed in a disconnected environment
Upgrade Red Hat OpenShift AI on OpenShift in a disconnected environment
Creating a workbench
Create a workbench and a custom image by using Custom Resource Definitions (CRDs) and the command-line.
Managing OpenShift AI
Cluster administrator tasks for managing OpenShift AI
Enabling the model registry component
Enabling the model registry component in Red Hat OpenShift AI Self-Managed
Working with machine learning features (Technology Preview)
Store, manage, and serve features to machine learning models with Feature Store
Enabling LAB-tuning (Technology Preview)
Enable model customization with LAB-tuning
Usage data collection notice
Learn about data collected in relation with your usage of the software
Documentation for OpenShift AI administrators
Guidance for managing OpenShift AI administration settings
Managing resources
Manage administration tasks from the OpenShift AI dashboard
Working with RAG (Developer Preview)
Working with RAG in Red Hat OpenShift AI Self-Managed
Working with accelerators
Working with accelerators from Red Hat OpenShift AI Self-Managed
Managing model registries
Managing model registries in Red Hat OpenShift AI Self-Managed
Documentation for OpenShift AI users
Guidance for ML Ops Engineers and Data Scientists on how to work in OpenShift AI
Getting started with Red Hat OpenShift AI Self-Managed
Learn how to work in an OpenShift AI environment
OpenShift AI tutorial - Fraud detection example
Use OpenShift AI to train an example model in JupyterLab, deploy the model, and refine the model by using automated pipelines
Working with data in an S3-compatible object store
Work with data stored in an S3-compatible object store from your workbench
Working on data science projects
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
Working in your data science IDE from Red Hat OpenShift AI Self-Managed
Working with data science pipelines
Work with data science pipelines from Red Hat OpenShift AI Self-Managed
Customizing models with LAB-tuning (Technology Preview)
Customize models with LAB-tuning
Monitoring data science models
Monitor your OpenShift AI models for fairness
Working with distributed workloads
Use distributed workloads for faster and more efficient data processing and model training
Working with connected applications
Connect to applications from Red Hat OpenShift AI Self-Managed
Working with model registries
Working with model registries in Red Hat OpenShift AI Self-Managed
Serving models
Serve models in Red Hat OpenShift AI Self-Managed
API tiers
View a list of API tiers and API version examples for OpenShift AI
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