Dataiku DSSInstall

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 Trained Model Details operation, the node retrieves detailed information about a trained machine learning model within a specified project, analysis, ML task, and model identifier.

This functionality is useful in scenarios where you want to automate the retrieval of metadata or details about trained models in your Dataiku projects, such as for monitoring, reporting, or further automated processing workflows.

Practical example:
You have an automated workflow that triggers after a model training completes. Using this node, you can fetch the detailed properties of the trained model (like performance metrics, parameters, or metadata) to log them, send notifications, or decide on deployment steps.

Properties

These properties must be provided to specify exactly which trained model's details to fetch.

Output

The output is a JSON object containing the detailed information of the trained model as returned by the Dataiku DSS API. This typically includes metadata such as:

• Model configuration and parameters
• Training results and performance metrics
• Model versioning information
• Any user metadata associated with the model

The exact structure depends on the Dataiku DSS API response for the trained model details endpoint.

The node does not output binary data for this operation.

Dependencies

• Requires valid Dataiku DSS API credentials, including:
  • The DSS server URL.
  • A user API key for authentication.
• The node makes HTTP requests to the Dataiku DSS REST API endpoints.
• No additional external dependencies beyond the n8n environment and the configured credentials.

Troubleshooting

• Missing Credentials Error: If the API credentials are not set or invalid, the node will throw an error indicating missing credentials.
• Required Parameter Missing: The node validates required parameters (Project Key, Analysis ID, ML Task ID, Model Full ID) and throws errors if any are missing.
• API Request Failures: Network issues, incorrect URLs, or permission problems may cause API request failures. The node surfaces these errors with messages prefixed by "Error calling Dataiku DSS API".
• Parsing Errors: If the API returns unexpected or malformed JSON, the node attempts to handle it gracefully but may return raw text instead.

To resolve common issues:

• Ensure all required input properties are correctly set.
• Verify that the API key has sufficient permissions to access the requested resources.
• Confirm the DSS server URL is reachable from the n8n instance.
• Check the correctness of IDs used (project, analysis, ML task, model).

Links and References

• Dataiku DSS API Documentation (official API reference)
• Dataiku Machine Learning Lab Documentation (for understanding ML tasks and models)

This summary focuses on the Machine Learning - Lab resource and the Get Trained Model Details operation as requested, based on static analysis of the provided source code and property definitions.