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Chapter 2. Product features
Red Hat OpenShift Data Science provides a number of features for data scientists and IT operations administrators.
2.1. Features for data scientists
- One-page JupyterHub notebook server configuration
- Choose from a default set of notebook images pre-configured with the tools and libraries you need for model development.
- Collaborate on notebooks using Git
- Use JupyterLab’s Git interface to work collaboratively with application developers or add other models to your notebooks.
- Integrate with Red Hat OpenShift Streams for Apache Kafka
- Integrate fault-tolerant real-time data streams into your notebooks and machine learning models by connecting OpenShift Data Science to Red Hat OpenShift Streams for Apache Kafka.
- Deploy using application templates
- Red Hat provides application templates designed for data scientists so that you can easily deploy your models and applications on OpenShift Dedicated for testing.
- Try it out in the Red Hat Developer sandbox environment
- You can try out OpenShift Data Science and access tutorials and activities in the Red Hat Developer sandbox environment.
2.2. Features for IT Operations administrators
- Install as an Add-on
- Sign up for a Field Trial and then install the OpenShift Data Science as an Add-on to your OpenShift Dedicated cluster using Red Hat Cluster Manager.
- Manage users with your existing identity provider
- OpenShift Data Science supports the same identity providers as OpenShift Dedicated. You can configure existing groups in your identity provider as administrators or users of OpenShift Data Science.
- Manage resources with OpenShift Dedicated
- Use your existing OpenShift Dedicated knowledge to configure and manage machine pools for your OpenShift Data Science users.
This section describes enhancements to existing features in Red Hat OpenShift Data Science.
- Default persistent volume claim (PVC) size increased
- The default size of a PVC provisioned for a data science user in an OpenShift Data Science cluster has been increased from 2 GB to 20 GB.
- Improved resilience to OpenShift Dedicated node failure
- OpenShift Data Science services now try to avoid being scheduled on the same node so that OpenShift Data Science components are more failure resistant.