Dataiku DSS

Overview

This node integrates with the Dataiku DSS API to perform various operations across many resources within a Dataiku project. Specifically, for the Machine Learning - Experiment Tracking resource and the List Models for a Run operation, it retrieves the list of machine learning models associated with a specific run ID in an experiment tracking system.

This is useful when you want to programmatically access models generated during a particular experiment run, enabling automation workflows such as model evaluation, deployment, or reporting based on experiment results.

Example use case:
You have an automated pipeline that triggers after a machine learning experiment completes. Using this node, you can fetch all models related to that run and then trigger further steps like model validation or deployment.

Properties

Output

The output is a JSON array where each item represents a model associated with the specified run. The exact structure depends on the Dataiku DSS API response but typically includes details about each model such as its metadata, parameters, and possibly performance metrics.

If the operation involves downloading files (not applicable here), binary data would be returned accordingly, but for this operation, the output is purely JSON.

Dependencies

• Requires a valid connection to a Dataiku DSS instance.
• Needs an API key credential for authentication with the Dataiku DSS API.
• The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
• The project key and run ID must be provided to identify the context of the request.

Troubleshooting

• Missing Credentials Error: If the API key or server URL is not set, the node will throw an error indicating missing credentials. Ensure the API key credential is properly configured.
• Missing Required Parameters: The node validates required parameters such as Project Key and Run ID. Omitting these will cause errors specifying which parameter is missing.
• API Request Failures: Network issues, incorrect URLs, or insufficient permissions may cause API call failures. Check connectivity and user permissions on the Dataiku DSS instance.
• Parsing Errors: If the API returns unexpected data formats, the node might fail to parse the response. Verify the API version compatibility and response format.

Links and References

• Dataiku DSS API Documentation – Official API reference for Dataiku DSS.
• MLflow Experiment Tracking API – Since the experiment tracking uses MLflow extension, this may provide additional context.
• Dataiku DSS Machine Learning Experiment Tracking – Overview of experiment tracking features in Dataiku DSS.

Note: This summary focuses on the "Machine Learning - Experiment Tracking" resource and the "List Models for a Run" operation as requested. The node supports many other resources and operations beyond this scope.