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 various Dataiku DSS resources. Specifically for the Machine Learning - Saved Model resource and the Set Version User Meta operation, it allows updating the user metadata associated with a specific version of a saved machine learning model within a project.
Common scenarios where this node is beneficial include:
- Managing metadata for different versions of machine learning models stored in Dataiku DSS.
- Automating updates to model version information as part of a CI/CD pipeline or model governance workflow.
- Enhancing model documentation or tracking by programmatically setting custom user metadata.
Practical example:
- You have multiple versions of a saved ML model in a project and want to tag a particular version with additional descriptive metadata (e.g., notes about training data, performance metrics, or deployment status). This node can update that metadata via the API.
Properties
| Name | Meaning |
|---|---|
| Project Key | The unique identifier of the Dataiku DSS project containing the saved model. |
| Save Model ID | The identifier of the saved model whose version metadata you want to update. |
| Version ID | The identifier of the specific version of the saved model to update. |
| Request Body | A JSON object containing the user metadata fields and values to set for the specified version. |
Output
The node outputs the response from the Dataiku DSS API after attempting to update the user metadata of the specified saved model version. The output is a JSON object representing the updated metadata or confirmation of the update.
If the operation involves downloading files or binary content (not applicable specifically for this operation), the node would output binary data accordingly. For this operation, the output is purely JSON.
Dependencies
- Requires an active connection to a Dataiku DSS instance.
- Requires valid API credentials: a server URL and an API key credential for authentication.
- The node uses HTTP requests to interact with the Dataiku DSS REST API endpoints.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing credentials, ensure that the Dataiku DSS API credentials are properly configured in n8n.
- Required Parameter Missing: Errors like "Project Key is required", "Save Model ID is required", or "Version ID is required" indicate that these mandatory inputs were not provided. Verify all required properties are set.
- API Request Failures: Network issues, incorrect URLs, or insufficient permissions may cause API call failures. Check the API endpoint URL, network connectivity, and user permissions in Dataiku DSS.
- Invalid JSON in Request Body: Ensure the
Request Bodyproperty contains valid JSON syntax; otherwise, the request will fail.
Links and References
- Dataiku DSS API Documentation
- Dataiku DSS Machine Learning Saved Models API
- n8n Documentation on Creating Custom Nodes
This summary focuses on the Machine Learning - Saved Model resource and the Set Version User Meta operation as requested, based on static analysis of the provided source code and input properties.