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docs |
Working On Data Science Projects |
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As a data scientist, you can organize your data science work into a single project. A data science project in {productname-short} can consist of the following components:
- Workbenches
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Creating a workbench allows you to work with models in your preferred IDE, such as JupyterLab.
- Cluster storage
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For data science projects that require data retention, you can add cluster storage to the project.
- Connections
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Adding a connection to your project allows you to connect data sources and sinks to your workbenches.
- Pipelines
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Standardize and automate machine learning workflows to enable you to further enhance and deploy your data science models.
- Models and model servers
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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
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Creating bias metrics allows you to monitor your machine learning models for bias.