Chapter 1. Overview of OpenShift Data Science
Using Red Hat OpenShift Data Science, users can integrate data, artificial intelligence and machine learning software to execute end-to-end machine learning workflows. OpenShift Data Science is supported in two configurations:
- Installed as an Add-on to a Red Hat managed environment such as Red Hat OpenShift Dedicated and Red Hat OpenShift Service on Amazon Web Services (ROSA).
- Installed as a self-managed Operator on a self-managed environment, such as Red Hat OpenShift Container Platform.
For data scientists, OpenShift Data Science includes Jupyter and a collection of default notebook images optimized with the tools and libraries required for model development, and the TensorFlow and PyTorch frameworks. Deploy and host your models, integrate models into external applications, and export models to host them in any hybrid cloud environment. You can also accelerate your data science experiments through the use of graphics processing units (GPUs).
For administrators, OpenShift Data Science enables data science workloads in an existing Red Hat OpenShift Dedicated or ROSA environment. Manage users with your existing OpenShift identity provider, and manage the resources available to notebook servers to ensure data scientists have what they require to create, train, and host models.