Preface
As a data scientist, you can organize your data science work into a single project. A data science project in OpenShift AI can consist of the following components:
- Workbenches
- Creating a workbench allows you to add a Jupyter notebook to your project.
- Cluster storage
- For data science projects that require data retention, you can add cluster storage to the project.
- Data connections
- Adding a data connection to your project allows you to connect data inputs to your workbenches.
- Pipelines
- Standardize and automate machine learning workflows to enable you to further enhance and deploy your data science models.
- Models and model servers
- Deploy a trained data science model to serve intelligent applications. Your model is deployed with an endpoint that allows applications to send requests to the model.
