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Overview

This node integrates with the Dataiku DSS API, enabling users to interact programmatically with various Dataiku DSS resources and operations. Specifically, for the Machine Learning - Lab resource and the Get Scoring Jar of Trained Model operation, it retrieves the scoring JAR file of a trained machine learning model. This is useful when you want to export the compiled Java scoring code of a model for deployment or integration into other systems that require Java-based scoring.

Common scenarios include:

  • Exporting a trained model's scoring JAR to deploy in a production environment outside Dataiku.
  • Automating the retrieval of model artifacts as part of a CI/CD pipeline.
  • Integrating model scoring capabilities into custom applications or services.

Properties

Name Meaning
Project Key The unique identifier of the Dataiku project containing the ML task and model.
Analysis ID The identifier of the analysis within the project related to the ML task.
ML Task ID The identifier of the machine learning task associated with the trained model.
Model Full ID The full identifier of the specific trained model whose scoring JAR is to be retrieved.
Query Parameters Optional additional query parameters to customize the API request (key-value pairs).

These properties are required to specify exactly which trained model's scoring JAR should be fetched from the Dataiku DSS instance.

Output

The output contains the binary data of the scoring JAR file corresponding to the specified trained model. This binary data represents the compiled Java archive used for optimized scoring of the model outside the Dataiku environment.

  • The binary data is returned under the binary.data field.
  • The filename for the binary data is set to "model_scoring.jar".

This allows downstream nodes or workflows to save, transfer, or deploy the scoring JAR as needed.

Dependencies

  • Requires an active connection to a Dataiku DSS instance.
  • Requires valid API credentials including the DSS server URL and an API key with sufficient permissions.
  • The node uses HTTP requests to communicate with the Dataiku DSS REST API.
  • No additional external libraries beyond those bundled with n8n are required.

Troubleshooting

  • Missing Credentials Error: If the API credentials are not provided or invalid, the node will throw an error indicating missing credentials.
  • Required Parameter Missing: The node validates required parameters such as Project Key, Analysis ID, ML Task ID, and Model Full ID. Omitting any of these will cause an error.
  • Permission Denied: If the API key does not have permission to access the specified project or model, the API call will fail.
  • Model Not Compatible: The scoring JAR is only available if the model supports optimized scoring and the user has the appropriate license.
  • Network Issues: Connectivity problems to the DSS server will result in request failures.
  • Unexpected Response Format: If the response cannot be parsed as JSON or binary, the node may throw an error.

To resolve errors:

  • Ensure all required input fields are correctly filled.
  • Verify API credentials and permissions.
  • Confirm the model supports scoring JAR export.
  • Check network connectivity and DSS server availability.

Links and References

This summary focuses on the "Machine Learning - Lab" resource and the "Get Scoring Jar of Trained Model" operation as requested.

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