Preface

As a data scientist, you can organize your data science work into a single project. A project in OpenShift AI can consist of the following components:

Workbenches: Creating a workbench allows you to work with models in your preferred IDE, such as JupyterLab.
Cluster storage: For projects that require data retention, you can add cluster storage to the project.
Connections: Adding a 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.
Bias metrics for models: Creating bias metrics allows you to monitor your machine learning models for bias.

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