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Dataiku DSS

Use the Dataiku DSS API

Actions364

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 Deploy Trained Model to Flow operation, it allows deploying a trained machine learning model from an ML task into a saved model and training recipe within a flow in Dataiku DSS.

This is beneficial in scenarios where automated deployment of machine learning models is required as part of a data pipeline or workflow, facilitating continuous integration and delivery of ML models. For example, after training a model in Dataiku's Machine Learning Lab, this node can deploy that model directly into a production flow for scoring or further processing without manual intervention.

Properties

Name Meaning
Project Key The key identifier of the Dataiku project containing the ML task and model.
Analysis ID The identifier of the analysis associated with the ML task.
ML Task ID The identifier of the machine learning task that produced the trained model.
Model Full ID The full identifier of the trained model to be deployed.
Request Body JSON object representing additional parameters or payload to send with the deployment request.

These properties are used to specify which trained model to deploy and provide any necessary configuration details for the deployment action.

Output

The node outputs the response from the Dataiku DSS API call related to the deployment action. The output is structured as JSON and typically contains information about the deployment status or confirmation of the action performed.

If the API returns binary data (not typical for this operation), it would be provided as binary output, but for the "Deploy Trained Model to Flow" operation, the output is JSON.

Example output structure:

{
  "deploymentStatus": "success",
  "details": {
    "modelId": "model_full_id_value",
    "flowRecipeId": "recipe_id_associated_with_deployment",
    "message": "Model deployed successfully"
  }
}

Dependencies

  • Requires an active connection to a Dataiku DSS instance.
  • Needs valid API credentials (an API key) for authentication with the Dataiku DSS API.
  • The node expects the Dataiku DSS server URL and user API key to be configured in the credentials.
  • No other external dependencies are required.

Troubleshooting

  • Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials are properly set up in n8n.
  • Required Parameter Errors: The node validates required parameters such as Project Key, Analysis ID, ML Task ID, and Model Full ID. Missing any of these will cause errors. Double-check that all required fields are filled.
  • API Call Failures: Network issues, incorrect server URLs, or invalid API keys can cause API call failures. Verify connectivity and credential validity.
  • Unexpected Response Format: If the API response cannot be parsed as JSON, check if the Dataiku DSS server is returning an error page or unexpected content.

Links and References

This summary focuses on the "Machine Learning - Lab" resource and the "Deploy Trained Model to Flow" operation, extracting relevant input properties, expected output, and usage context based on static code analysis of the provided source.

Discussion