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, enabling users to perform a wide range of operations on Dataiku DSS resources. Specifically for the Machine Learning - Experiment Tracking resource, it supports creating virtual datasets that expose experiment tracking data from a project.
Use cases include automating interactions with Dataiku DSS projects, managing machine learning experiments, datasets, models, and other resources programmatically within n8n workflows. For example, you can create a virtual dataset that aggregates experiment tracking information, which can then be used downstream for reporting or further analysis.
The Create Virtual Dataset operation under the Machine Learning - Experiment Tracking resource allows you to generate a virtual dataset representing experiment tracking data for a specified project key.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project where the virtual dataset will be created. |
| Request Body | JSON object containing the details and configuration for the virtual dataset creation request. |
Output
- The output is a JSON array where each item corresponds to the response from the Dataiku DSS API.
- For the Create Virtual Dataset operation, the output JSON contains the result of the virtual dataset creation request, typically including metadata about the created dataset.
- The node does not output binary data for this operation.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires credentials providing:
- The URL or address of the Dataiku DSS server.
- A valid API key for authentication.
- The node uses HTTP requests to interact with the Dataiku DSS REST API endpoints.
- No additional external services are required beyond the Dataiku DSS API.
Troubleshooting
- Missing Credentials Error: If the node throws "Missing Dataiku DSS API Credentials," ensure that the API key credential is configured correctly in n8n.
- Required Parameter Errors: The node validates required parameters such as Project Key before making API calls. If you see errors like "Project Key is required," verify that you have provided all mandatory inputs.
- API Request Failures: Errors returned by the Dataiku DSS API (e.g., authorization failures, invalid request body) will be surfaced as node errors. Check the error message and stack trace for details.
- Invalid JSON in Request Body: Ensure that the JSON provided in the Request Body property is well-formed and matches the expected schema for the virtual dataset creation endpoint.
- Network Issues: Verify network connectivity to the Dataiku DSS server and that the server is accessible from the n8n environment.
Links and References
This summary focuses on the Machine Learning - Experiment Tracking resource and the Create Virtual Dataset operation as requested.