Red Hat OpenShift AI Self-Managed
2.25
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
Last Updated: 2025-10-28
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Red Hat OpenShift AI Self-Managed
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
Last Updated: 2025-10-
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Abstract
Red Hat OpenShift AI is a platform for data scientists and developers of artificial intelligence and
machine learning applications.
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Table of Contents
CHAPTER 1. OVERVIEW OF OPENSHIFT AI CHAPTER 2. PRODUCT FEATURES 2.1. FEATURES FOR DATA SCIENTISTS 2.2. FEATURES FOR OPENSHIFT CLUSTER ADMINISTRATORS CHAPTER 3. TRY IT CHAPTER 4. GET IT 3 4 4 5 6 7 Table of Contents
CHAPTER 1. OVERVIEW OF OPENSHIFT AI Red Hat OpenShift AI is a platform for data scientists and developers of artificial intelligence and machine learning (AI/ML) applications. OpenShift AI provides an environment to develop, train, serve, test, and monitor AI/ML models and applications on-premise or in the cloud. For data scientists, OpenShift AI includes Jupyter and a collection of default workbench images optimized with the tools and libraries required for model development, and the TensorFlow and PyTorch frameworks. Deploy and host your models, integrate models into external applications, and export models to host them in any hybrid cloud environment. You can enhance your data science projects on OpenShift AI by building portable machine learning (ML) workflows with data science pipelines, using Docker containers. You can also accelerate your data science experiments through the use of graphics processing units (GPUs) and Intel Gaudi AI accelerators. For administrators, OpenShift AI enables data science workloads in an existing Red Hat OpenShift or ROSA environment. Manage users with your existing OpenShift identity provider, and manage the resources available to workbenches to ensure data scientists have what they require to create, train, and host models. Use accelerators to reduce costs and allow your data scientists to enhance the performance of their end-to-end data science workflows using graphics processing units (GPUs) and Intel Gaudi AI accelerators. OpenShift AI has two deployment options: Self-managed software that you can install on-premise or in the cloud. You can install OpenShift AI Self-Managed in a self-managed environment such as OpenShift Container Platform, or in Red Hat-managed cloud environments such as Red Hat OpenShift Dedicated (with a Customer Cloud Subscription for AWS or GCP), Red Hat OpenShift Service on Amazon Web Services (ROSA classic or ROSA HCP), or Microsoft Azure Red Hat OpenShift. A managed cloud service, installed as an add-on in Red Hat OpenShift Dedicated (with a Customer Cloud Subscription for AWS or GCP) or in Red Hat OpenShift Service on Amazon Web Services (ROSA classic). For information about OpenShift AI Cloud Service, see Product Documentation for Red Hat OpenShift AI. For information about OpenShift AI supported software platforms, components, and dependencies, see the Red Hat OpenShift AI: Supported Configurations Knowledgebase article. For a detailed view of the 2.25 release lifecycle, including the full support phase window, see the Red Hat OpenShift AI Self-Managed Life Cycle Knowledgebase article. CHAPTER 1. OVERVIEW OF OPENSHIFT AI
CHAPTER 2. PRODUCT FEATURES Red Hat OpenShift AI provides several features for data scientists and OpenShift cluster administrators. 2.1. FEATURES FOR DATA SCIENTISTS Containers While tools such as JupyterLab already offer intuitive ways for data scientists to develop models on their machines, there are always inherent complexities involved with collaboration and sharing work. Moreover, using specialized hardware such as powerful GPUs can be very expensive when you have to buy and maintain your own. The Jupyter environment that is included with OpenShift AI lets you take your development environment anywhere you need it to be. Because all of the workloads are run as containers, collaboration is as easy as sharing an image with your team members, or even simply adding it to the list of default containers that they can use. As a result, GPUs and large amounts of memory are significantly more accessible, since you are no longer limited by what your laptop can support. Integration with third-party machine learning tools We have all run into situations where our favorite tools or services do not play well with one another. OpenShift AI is designed with flexibility in mind. You can use a wide range of open source and third- party tools with OpenShift AI. These tools support the complete machine learning lifecycle, from data engineering and feature extraction to model deployment and management. Collaboration on Jupyter notebooks with Git Use Jupyter’s Git interface to work collaboratively with others, and track the changes to your code. Securely built workbench images Choose from a default set of workbench images that are pre-configured with the tools and libraries that you need for model development. Software stacks, especially those involved in machine learning, tend to be complex systems. There are many modules and libraries in the Python ecosystem that can be used, so determining which versions of what libraries to use can be very challenging. OpenShift AI includes many packaged workbench images that have been built with insight from data scientists and recommendation engines. You can start new projects quickly on the right foot without worrying about downloading unproven and possibly insecure images from random upstream repositories. Custom workbench images In addition to workbench images provided and supported by Red Hat and independent software vendors (ISVs), you can configure custom workbench images that cater to your project’s specific requirements. Data science pipelines OpenShift AI with data science pipelines 2.0 provides an efficient way to run your data science workloads. You can standardize and automate machine learning workflows that enable you to develop and deploy your data science models. Model serving As a data scientist, you can deploy your trained machine-learning models to serve intelligent applications in production. Deploying or serving a model makes the model’s functions available as a service endpoint that can be used for testing or integration into applications. You have much control over how this serving is performed. Optimize your data science models with accelerators If you work with large data sets, you can optimize the performance of your data science models in OpenShift AI with NVIDIA graphics processing units (GPUs) or Intel Gaudi AI accelerators. Accelerators enable you to scale your work, reduce latency, and increase productivity. Red Hat OpenShift AI Self-Managed 2.25 Introduction to Red Hat OpenShift AI
CHAPTER 3. TRY IT Data scientists and developers can try OpenShift AI and access tutorials and activities in the Red Hat Developer Sandbox environment. OpenShift cluster administrators can try OpenShift AI in your own cluster with a 60-day product trial. Red Hat OpenShift AI Self-Managed 2.25 Introduction to Red Hat OpenShift AI
CHAPTER 4. GET IT Managed cloud service You have the following options for subscribing to OpenShift AI as a managed service: For OpenShift Dedicated, subscribe through Red Hat. For Red Hat OpenShift Service on Amazon Web Services (ROSA), subscribe through Red Hat or subscribe through the AWS Marketplace. Self-managed software To get Red Hat OpenShift AI as self-managed software, sign up for it with your Red Hat account team. CHAPTER 4. GET IT