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 - Saved Model resource and the Evaluate MLflow Model Version operation, it allows evaluating the performance of a specific version of an MLflow model stored as a saved model in a project on a particular dataset.
Typical use cases include:
- Automating model evaluation workflows within Dataiku projects.
- Triggering model performance assessments programmatically after training or deployment.
- Integrating model evaluation results into broader data pipelines or dashboards.
For example, a user can specify a saved model ID and version ID along with evaluation parameters to run an evaluation job on a dataset, receiving back metrics or results that describe the model's predictive quality.
Properties
| Name | Meaning |
|---|---|
| Project Key | The key identifier of the Dataiku project containing the saved model. |
| Save Model ID | The unique identifier of the saved model to evaluate. |
| Version ID | The specific version of the saved model to evaluate. |
| Request Body | JSON object containing the evaluation request parameters, such as dataset details or options. |
Output
The node outputs the response from the Dataiku DSS API call related to the evaluation of the MLflow model version. This output is provided in the json field of the node's output data and typically contains evaluation metrics, status, or detailed results of the model evaluation process.
If the API returns binary data (not typical for this operation), it would be prepared accordingly, but for this operation, the output is expected to be JSON-formatted evaluation results.
Dependencies
- Requires valid credentials for the Dataiku DSS API, including the server URL and an API key.
- The node uses HTTP requests to interact with the Dataiku DSS REST API.
- Proper permissions on the Dataiku project and saved model are necessary to perform the evaluation.
Troubleshooting
- Missing Credentials Error: If the node throws an error about missing API credentials, ensure that the Dataiku DSS API credentials are configured correctly in n8n.
- Required Parameter Errors: The node validates required parameters like Project Key, Save Model ID, and Version ID. Missing any of these will cause errors. Double-check that all required fields are filled.
- API Request Failures: Network issues, incorrect URLs, or insufficient permissions may cause API call failures. Review the error message for details and verify connectivity and access rights.
- Invalid JSON in Request Body: The Request Body must be valid JSON. Malformed JSON will cause parsing errors.
Links and References
- Dataiku DSS API Documentation
- MLflow Model Management in Dataiku
- n8n Documentation on HTTP Request Node
This summary focuses on the "Machine Learning - Saved Model" resource and the "Evaluate MLflow Model Version" operation as requested, based on static analysis of the provided source code and property definitions.