Overview Use Cases Connectors Authentication Getting Started with the Python SDK API API Reference ai_agents ai_chat algorithm annotations application_connectors batch_prediction code_completion custom_loss_function custom_metric database_connectors dataset deployment deployment_conversations docstore document_retrievers documentation eda external_app feature_drift feature_group feature_group_row_process feature_group_template file_connectors graph_dashboard holdout_analysis llm_apps mcp_server_connections model createModelFromPython createTrainTestDataSplitFeatureGroup deleteModel deleteModelVersion describeModel describeModelArtifactsExport describeModelVersion describeTrainTestDataSplitFeatureGroup describeTrainTestDataSplitFeatureGroupVersion exportCustomModelVersion exportModelArtifactAsFeatureGroup getCustomTrainFunctionInfo getFeatureGroupSchemasForModelVersion getFeatureImportanceByModelVersion getModelMetrics getTrainingConfigOptions getTrainingDataLogs getTrainingLogs listModelArtifactsExports listModelVersions listModels renameModel retrainModel setDefaultModelAlgorithm setModelObjective setModelPredictionParams setModelTrainingConfig trainModel updatePythonModel updatePythonModelGit updatePythonModelZip model_monitoring module natural_language_explanation organization pipelines predict prediction_operator project project_scopes python_function refresh routellm secrets streaming streaming_connectors upload user webhooks API Classes API Inputs Documentation Chat Bot API Search How to

Method

getTrainingDataLogs GET

Copy GET

Retrieves the data preparation logs during model training.

Arguments:

REQUIRED	KEY	TYPE	DESCRIPTION
Yes	modelVersion	str	The unique version ID of the model version.

Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY	TYPE	DESCRIPTION
success	Boolean	true if the call succeeded, false if there was an error
result	list[DataPrepLogs]

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