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Dataiku DSS

Use the Dataiku DSS API

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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 - Lab resource and the List Visual Analyses operation, it allows listing all visual analyses within a specified project. This is useful for data scientists and analysts who want to programmatically retrieve and manage visual analysis objects in their machine learning projects.

Common scenarios include:

  • Automating retrieval of visual analyses metadata for reporting or auditing.
  • Integrating visual analysis listings into larger workflows for model evaluation or monitoring.
  • Synchronizing visual analysis information between Dataiku DSS and other systems.

Example: A user can list all visual analyses in a project to display them in a custom dashboard or trigger further processing based on the analyses available.

Properties

Name Meaning
Project Key The unique identifier of the Dataiku DSS project where the visual analyses reside.

Note: The provided input properties JSON only includes "Project Key" relevant for this operation.

Output

The output is a JSON array where each item represents a visual analysis object retrieved from the Dataiku DSS project. The exact structure depends on the Dataiku DSS API response but typically includes details such as:

  • Visual analysis ID
  • Name and description
  • Creation and modification timestamps
  • Associated ML tasks or models
  • Other metadata related to the visual analysis

No binary data output is expected for this operation.

Dependencies

  • Requires an active connection to a Dataiku DSS instance.
  • Requires 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.

Troubleshooting

  • Missing Credentials Error: If the API credentials are not set or invalid, the node will throw an error indicating missing credentials. Ensure that the API key credential is properly configured.
  • Missing Project Key: The operation requires a project key; if omitted, the node throws an error stating that the project key is required.
  • API Request Failures: Network issues, incorrect server URLs, or insufficient permissions may cause API request failures. Check connectivity and user permissions.
  • Unexpected Response Format: If the API returns unexpected data, parsing errors might occur. Verify the API version compatibility and response format.

Links and References


This summary focuses on the Machine Learning - Lab resource and the List Visual Analyses operation as requested, based on static analysis of the provided source code and input property definitions.

Discussion