Dataiku DSS icon

Dataiku DSS

Use the Dataiku DSS API
Join our community

Actions364

Overview

This node integrates with the Dataiku DSS API, specifically focusing on the "Machine Learning - Lab" resource and the operation "Adjust Forecasting Parameters and Algorithm." It allows users to adjust forecasting parameters and algorithms for machine learning tasks within a Dataiku DSS project. This is useful in scenarios where you want to fine-tune or update forecasting models programmatically as part of an automated workflow.

Practical examples include:

  • Automatically adjusting forecasting parameters after new data is ingested.
  • Integrating model tuning steps into a larger data pipeline.
  • Triggering algorithm adjustments based on external events or schedules.

Properties

Name Meaning
Project Key The unique identifier of the Dataiku DSS project where the ML task resides.
Analysis ID The identifier of the analysis within the project related to the forecasting adjustment.
ML Task ID The identifier of the specific machine learning task to adjust forecasting parameters for.
Request Body A JSON object containing the details of the forecasting parameters and algorithm settings to apply during adjustment.

Output

The node outputs the response from the Dataiku DSS API call as JSON in the json field. This typically contains the result of the forecasting parameters adjustment operation, such as confirmation of success or details about the updated model/task.

If the operation involves downloading files (not applicable here), the node would output binary data representing those files. For this operation, the output is purely JSON.

Dependencies

  • Requires an active connection to a Dataiku DSS instance.
  • Needs valid API credentials: a server URL and an API key credential for authentication.
  • The node uses HTTP requests to communicate with the Dataiku DSS REST API.

Troubleshooting

  • Missing Credentials: If the API credentials are not set or invalid, the node will throw an error indicating missing credentials.
  • Required Parameters Missing: The node validates required parameters like Project Key, Analysis ID, and ML Task ID. Omitting any of these will cause an error.
  • API Errors: Errors returned by the Dataiku DSS API (e.g., invalid project or task IDs, permission issues) will be surfaced as node errors with descriptive messages.
  • Invalid JSON in Request Body: The request body must be valid JSON; otherwise, the node will fail to parse it before sending.

To resolve errors:

  • Ensure all required input properties are provided.
  • Verify that the API key and server URL are correct and have sufficient permissions.
  • Validate the JSON structure in the Request Body property.

Links and References

This summary focuses on the "Machine Learning - Lab" resource and the "Adjust Forecasting Parameters and Algorithm" operation as requested.

Discussion