Chapter 2. New features and enhancements

OpenShift AI model registry and model catalog are now available as general availability (GA) features.

A model registry acts as a central repository for administrators and data scientists to register, version, and manage the lifecycle of AI models before configuring them for deployment. A model registry is a key component for AI model governance.

The model catalog provides a curated library where data scientists and AI engineers can discover and evaluate the available generative AI models to find the best fit for their use cases.

The LLM Compressor library is now generally available and fully integrated into standard OpenShift AI workbench images and pipelines.

This library provides a supported, integrated method to optimize large language models for improved inference, particularly for deployment on vLLM, without leaving your OpenShift AI environment. You can run model compression as an interactive notebook task or as a batch job in a pipeline, which significantly reduces the hardware costs and improves the inference speeds of their generative AI workloads.

You can now configure OpenShift AI to use an existing Argo Workflows instance instead of the one included with Data Science Pipelines. This feature supports users who maintain their own Argo Workflows environments and simplifies adoption of pipelines on clusters where Argo Workflows is already deployed.

A new global configuration option disables deployment of the embedded Argo WorkflowControllers, allowing clusters that already use Argo Workflows to integrate with pipelines without conflicts. Cluster administrators can choose whether to deploy the embedded controllers or use their own Argo instance and manage both lifecycles independently. For more information, see Configuring pipelines with your own Argo Workflows instance.

You can now customize the CPU and memory requests and limits for the OAuth proxy sidecar in workbench pods. To do this, add one or more of the following annotations to the notebooks custom resource (CR):

You can now define and manage data science pipelines and pipeline versions by using the Kubernetes API, which stores them as custom resources in the cluster instead of the internal database. This enhancement makes it easier to use OpenShift GitOps (Argo CD) or similar tools to manage pipelines, while still allowing you to manage them through the OpenShift AI user interface, API, and kfp SDK.

This option, enabled by default, is configurable with the Store pipeline definitions in Kubernetes checkbox when you create or edit a pipeline server. OpenShift AI administrators and project owners can also configure this option by setting the spec.apiServer.pipelineStore field to kubernetes or database in the DataSciencePipelinesApplication (DSPA) custom resource. For more information, see Defining a pipeline by using the Kubernetes API.