Dataiku DSS

Overview

This node interacts with the Dataiku DSS API to perform various operations related to machine learning saved models, specifically including retrieving metadata for a specific MLflow model version. It is useful in scenarios where users need to programmatically access detailed metadata about a particular version of a saved machine learning model managed within Dataiku DSS, such as for auditing, monitoring, or integration purposes.

For example, a data scientist might use this node to fetch metadata about a deployed MLflow model version to verify its parameters and status before triggering further automated workflows or reporting.

Properties

Output

The node outputs JSON data containing the metadata of the specified MLflow model version. This metadata typically includes details such as model parameters, creation timestamps, tags, and other descriptive information associated with that version in MLflow.

If the operation involves downloading files (not applicable here), the node can output binary data representing those files, but for this operation, the output is purely JSON metadata.

Dependencies

• Requires an active connection to a Dataiku DSS instance.
• Requires valid API credentials (an API key) for authenticating requests to the Dataiku DSS API.
• The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.

Troubleshooting

• Missing Credentials: If the API credentials are not provided or invalid, the node will throw an error indicating missing credentials.
• Required Parameters Missing: The node validates required parameters such as Project Key, Save Model ID, and Version ID. Omitting any of these will cause an error specifying which parameter is missing.
• API Errors: Errors returned from the Dataiku DSS API (e.g., 404 if the model version does not exist) will be surfaced as node errors with messages prefixed by "Error calling Dataiku DSS API".
• Parsing Errors: If the response from the API is not valid JSON when expected, the node attempts to handle it gracefully but may throw parsing errors.

To resolve issues:

• Ensure all required input properties are correctly set.
• Verify that the API key credential has sufficient permissions.
• Confirm that the project, saved model, and version IDs exist and are correct.
• Check network connectivity to the Dataiku DSS server.

Links and References

• Dataiku DSS API Documentation
• MLflow Model Registry Concepts
• Dataiku DSS Machine Learning Saved Models