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Databricks Ml Experiments Log Model

Tool to log a model artifact for an MLflow run (Experimental API). Use when you need to record model metadata including artifact paths, flavors, and versioning information for a training run. The model_json parameter should contain a complete MLmodel specification in JSON string…

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Use Databricks Ml Experiments Log Model

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Inputs

  • run_idstring
    Required
    ID of the run to log the model under. This field is required
  • model_jsonstring
    Required
    MLmodel file in JSON format as a string. Contains model metadata including artifact_path, flavors, mlflow_version, model_uuid, and utc_time_created. This field is required

Observable output

  • data
    Required
    Data from the action execution
  • errorstring
    Error if any occurred during the execution of the action
  • successfulboolean
    Required
    Whether or not the action execution was successful or not

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