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Chapter 1. Architecture of OpenShift AI Self-Managed

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

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, workbench images, accelerator profiles, hardware profiles, and model-serving runtimes. Data scientists can use the dashboard to create projects to organize their data science work.

    Important

    By default, hardware profiles are hidden in the dashboard navigation menu and user interface, while accelerator profiles remain visible. In addition, user interface components associated with the deprecated accelerator profiles functionality are still displayed. To show the Settings Hardware profiles option in the dashboard navigation menu, and the user interface components associated with hardware profiles, set the disableHardwareProfiles value to false in the OdhDashboardConfig custom resource (CR) in OpenShift. For more information about setting dashboard configuration options, see Customizing the dashboard.

    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 2.0, using Docker containers. With data science pipelines, data scientists can automate workflows as they develop their data science models.
    Jupyter (self-managed)
    A self-managed application that allows data scientists to configure a basic standalone workbench 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.

    Retrieval-Augmented Generation (RAG)
    Data scientists and AI engineers can leverage Retrieval-Augmented Generation (RAG) capabilities provided by the integrated Llama Stack Operator. By combining large language model inference, semantic retrieval, and vector database storage, data scientists and AI engineers can obtain tailored, accurate, and verifiable answers to complex queries based on their own datasets within a data science project.

  • 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.

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

  • The redhat-ods-operator project contains the Red Hat OpenShift AI Operator.
  • The redhat-ods-applications project includes the dashboard and other required components of OpenShift AI.
  • The rhods-notebooks project is where basic workbenches are deployed by default.

You can specify custom projects if needed. You or your data scientists must also 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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