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Chapter 1. Architecture of OpenShift AI Self-Managed

Red Hat OpenShift AI Self-Managed is an Operator that is available on a self-managed environment, such as Red Hat OpenShift Container Platform.

OpenShift AI integrates the following components and services:

  • At the service layer:

    OpenShift AI dashboard
    A customer-facing dashboard that shows available and installed applications for the OpenShift AI environment as well as learning resources such as tutorials, quick starts, and documentation. Administrative users can access functionality to manage users, clusters, notebook images, accelerator profiles, and model-serving runtimes. Data scientists can use the dashboard to create projects to organize their data science work.
    Model serving
    Data scientists can deploy trained machine-learning models to serve intelligent applications in production. After deployment, applications can send requests to the model using its deployed API endpoint.
    Data science pipelines
    Data scientists can build portable machine learning (ML) workflows with data science pipelines, using Docker containers. This enables your data scientists to automate workflows as they develop their data science models.
    Jupyter (self-managed)
    A self-managed application that allows data scientists to configure their own notebook server environment and develop machine learning models in JupyterLab.
    Distributed workloads
    Data scientists can use multiple nodes in parallel to train machine-learning models or process data more quickly. This approach significantly reduces the task completion time, and enables the use of larger datasets and more complex models.
Important

The distributed workloads feature is currently available in Red Hat OpenShift AI 2.8 as Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

  • At the management layer:

    The Red Hat OpenShift AI Operator
    A meta-operator that deploys and maintains all components and sub-operators that are part of OpenShift AI.
    Monitoring services
    Prometheus gathers metrics from OpenShift AI for monitoring purposes.

When you install the Red Hat OpenShift AI Operator in the OpenShift Container Platform cluster, the following new projects are created:

  • The redhat-ods-operator project contains the Red Hat OpenShift AI Operator.
  • The redhat-ods-applications project installs the dashboard and other required components of OpenShift AI.
  • The redhat-ods-monitoring project contains services for monitoring.
  • The rhods-notebooks project is where notebook environments are deployed by default.

You or your data scientists must create additional projects for the applications that will use your machine learning models.

Do not install independent software vendor (ISV) applications in namespaces associated with OpenShift AI.

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