Actions364
- Continuous Activity Actions
- Dataset Actions
- Get Last Metric Values
- Get Metadata
- Get Schema
- Get Single Metric History
- List Datasets
- List Partitions
- Compute Metrics
- Create Dataset
- Create Managed Dataset
- Delete Data
- Delete Dataset
- Execute Tables Import
- Get Column Lineage
- Get Data
- Get Data - Alternative Version
- Get Dataset Settings
- Get Full Info
- List Tables
- List Tables Schemas
- Prepare Tables Import
- Run Checks
- Set Metadata
- Set Schema
- Synchronize Hive Metastore
- Update Dataset Settings
- Update From Hive Metastore
- API Service Actions
- Bundles Automation-Side Actions
- Bundles Design-Side Actions
- Connection Actions
- Dashboard Actions
- Data Collection Actions
- Data Quality Actions
- Compute Rules on Specific Partition
- Create Data Quality Rules Configuration
- Delete Rule
- Get Data Quality Project Current Status
- Get Data Quality Project Timeline
- Get Data Quality Rules Configuration
- Get Dataset Current Status
- Get Dataset Current Status per Partition
- Get Last Outcome on Specific Partition
- Get Last Rule Results
- Get Rule History
- Update Rule Configuration
- DSS Administration Actions
- Job Actions
- Library Actions
- Dataset Statistic Actions
- Discussion Actions
- Flow Documentation Actions
- Insight Actions
- Internal Metric Actions
- LLM Mesh Actions
- Machine Learning - Lab Actions
- Delete Visual Analysis
- Deploy Trained Model to Flow
- Download Model Documentation of Trained Model
- Generate Model Documentation From Custom Template
- Start Training ML Task
- Update User Metadata for Trained Model
- Update Visual Analysis
- Adjust Forecasting Parameters and Algorithm
- Compute Partial Dependencies of Trained Model
- Compute Subpopulation Analysis of Trained Model
- Create ML Task
- Create Visual Analysis
- Create Visual Analysis and ML Task
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get ML Task Settings
- Get ML Task Status
- Get Model Snippet
- Get Partial Dependencies of Trained Model
- Get Scoring Jar of Trained Model
- Get Scoring PMML of Trained Model
- Get Subpopulation Analysis of Trained Model
- Get Trained Model Details
- Get Visual Analysis
- List ML Tasks of Project
- List ML Tasks of Visual Analyses
- List Visual Analyses
- Reguess ML Task
- Machine Learning - Saved Model Actions
- Compute Partial Dependencies of Version
- Get Version Scoring PMML
- Get Version Snippet
- Import MLflow Version From File or Path
- List Saved Models
- List Versions
- Set Version Active
- Compute Subpopulation Analysis of Version
- Create Saved Model
- Delete Version
- Download Model Documentation of Version
- Evaluate MLflow Model Version
- Generate Model Documentation From Custom Template
- Generate Model Documentation From Default Template
- Generate Model Documentation From File Template
- Get MLflow Model Version Metadata
- Get Partial Dependencies of Version
- Get Saved Model
- Get Subpopulation Analysis of Version
- Get Version Details
- Get Version Scoring Jar
- Set Version User Meta
- Update Saved Model
- Long Task Actions
- Machine Learning - Experiment Tracking Actions
- Macro Actions
- Plugin Actions
- Download Plugin
- Fetch From Git Remote
- Get File Detail From Plugin
- Get Git Remote Info
- Get Plugin Settings
- Install Plugin From Git
- Install Plugin From Store
- List Files in Plugin
- List Git Branches
- List Plugin Usages
- Move File or Folder in Plugin
- Add Folder to Plugin
- Create Development Plugin
- Create Plugin Code Env
- Delete File From Plugin
- Delete Git Remote Info
- Delete Plugin
- Download File From Plugin
- Move Plugin to Dev Environment
- Pull From Git Remote
- Push to Git Remote
- Rename File or Folder in Plugin
- Reset to Local Head State
- Reset to Remote Head State
- Set Git Remote Info
- Set Plugin Settings
- Update Plugin Code Env
- Update Plugin From Git
- Update Plugin From Store
- Update Plugin From Zip Archive
- Upload File to Plugin
- Upload Plugin
- Project Deployer Actions
- Get Deployment Settings
- Get Deployment Status
- Create Deployment
- Create Infra
- Create Project
- Delete Bundle
- Delete Deployment
- Delete Infra
- Delete Project
- Get Deployment
- Get Deployment Governance Status
- Get Infra
- Get Infra Settings
- Get Project
- Get Project Settings
- Save Deployment Settings
- Save Infra Settings
- Save Project Settings
- Update Deployment
- Upload Bundle
- SQL Query Actions
- Wiki Actions
- Managed Folder Actions
- Meaning Actions
- Model Comparison Actions
- Notebook Actions
- Project Actions
- Project Folder Actions
- Recipe Actions
- Scenario Actions
- Security Actions
- Streaming Endpoint Actions
- Webapp Actions
- Workspace Actions
Overview
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.