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 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 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.
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-operatorproject contains the Red Hat OpenShift AI Operator. -
The
redhat-ods-applicationsproject installs the dashboard and other required components of OpenShift AI. -
The
redhat-ods-monitoringproject contains services for monitoring. -
The
rhods-notebooksproject 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.
