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Machine Learning - Experiment Tracking → Set Inference Information
AI-generatedOverview
This node integrates with the Dataiku DSS API, specifically focusing on the "Machine Learning - Experiment Tracking" resource and its "Set Inference Information" operation. It allows users to set inference-related metadata for a machine learning run within a project. This is useful in scenarios where you want to annotate or update the type of model used in an experiment run, along with other relevant information that can assist in deployment or evaluation workflows.
Practical examples include:
- Marking a specific ML run as an inference run with details about the model type.
- Updating inference metadata to help downstream systems understand how to handle the model output.
- Automating the management of experiment tracking metadata as part of a CI/CD pipeline for ML models.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku project containing the experiment run. |
| Request Body | JSON object containing the inference information to set for the experiment run. |
The Request Body property expects a JSON structure with the inference information details. The exact schema depends on the Dataiku DSS API specification for setting inference info.
Output
The node outputs the response from the Dataiku DSS API call in the json field. This typically contains confirmation or details about the updated inference information for the specified run.
If the API returns binary data (not typical for this operation), it would be provided in the binary field, but for this operation, the output is JSON.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Needs an API key credential for authentication with the Dataiku DSS API.
- The node uses HTTP requests to communicate with the Dataiku DSS REST API endpoints.
Troubleshooting
- Missing Credentials Error: If the API key or server URL is not configured, the node will throw an error indicating missing credentials. Ensure the Dataiku DSS API credentials are properly set up in n8n.
- Required Parameter Missing: The node validates required parameters such as Project Key before making the API call. If these are missing, it throws descriptive errors. Make sure all required fields are filled.
- API Errors: If the Dataiku DSS API returns an error (e.g., invalid project key, malformed request body), the node will throw an error with the message from the API. Check the request body format and parameter values.
- Network Issues: Connectivity problems to the Dataiku DSS server will cause request failures. Verify network access and server availability.
Links and References
- Dataiku DSS API Documentation — Official API docs for detailed request/response schemas.
- Experiment Tracking API Reference — Specific section for experiment tracking operations including inference info.
- n8n Documentation — For general usage of nodes and credentials setup.
This summary focuses on the "Machine Learning - Experiment Tracking" resource and the "Set Inference Information" operation as requested, based on static analysis of the provided source code and properties.