# Data exports > Data exports - Export prediction, actuals, training, and data quality data from deployments with the > Python API client. This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context. Companion generated at `2026-04-24T16:03:56.282607+00:00` (UTC). ## Primary page - [Data exports](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html): Full documentation for this topic (HTML). ## Sections on this page - [Prediction data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#prediction-data-export): In-page section heading. - [Create a prediction data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#create-a-prediction-data-export): In-page section heading. - [List prediction data exports](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#list-prediction-data-exports): In-page section heading. - [Retrieve a prediction data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#retrieve-a-prediction-data-export): In-page section heading. - [Fetch prediction export datasets](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#fetch-prediction-export-datasets): In-page section heading. - [Actuals data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#actuals-data-export): In-page section heading. - [Create actuals data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#create-actuals-data-export): In-page section heading. - [List actuals data exports](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#list-actuals-data-exports): In-page section heading. - [Retrieve actuals data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#retrieve-actuals-data-export): In-page section heading. - [Fetch actuals export datasets](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#fetch-actuals-export-datasets): In-page section heading. - [Training data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#training-data-export): In-page section heading. - [Create training data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#create-training-data-export): In-page section heading. - [List training data exports](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#list-training-data-exports): In-page section heading. - [Retrieve a training data export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#retrieve-a-training-data-export): In-page section heading. - [Fetch a training export dataset](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#fetch-a-training-export-dataset): In-page section heading. - [Data quality export](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#data-quality-export): In-page section heading. - [Data quality export list](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/data_exports.html#data-quality-export-list): In-page section heading. ## Related documentation - [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page. - [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page. - [Python API client user guide](https://docs.datarobot.com/en/docs/api/dev-learning/python/index.html): Linked from this page. - [MLOps](https://docs.datarobot.com/en/docs/api/dev-learning/python/mlops/index.html): Linked from this page. ## Documentation content # Data exports Use deployment data export to retrieve data sent for predictions along with the associated predictions. ## Prediction data export The following sections outline how to manage prediction data exports. ### Create a prediction data export To create a prediction data export, use `PredictionDataExport.create`, defining the time window to include in the export using the `start` and `end` parameters: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now) ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265', start=now - timedelta(days=7), end=now ) ``` For deployments in batch mode, provide batch IDs to export prediction data for those batches: ``` from datetime import datetime, timedelta from datarobot.models.deployment import PredictionDataExport now=datetime.now() prediction_data_export = PredictionDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265', start=now - timedelta(days=7), end=now, batch_ids=['6572db2c9f9d4ad3b9de33d0', '6572db2c9f9d4ad3b9de33d0'] ) ``` The `start` and `end` of the export can be defined as a datetime or string type. ### List prediction data exports To list prediction data exports, use `PredictionDataExport.list`: ``` from datarobot.models.deployment import PredictionDataExport prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=0) prediction_data_exports >>> [PredictionDataExport('65fbe59aaa3f847bd5acc75b'), PredictionDataExport('65fbe59aaa3f847bd5acc75c'), PredictionDataExport('65fbe59aaa3f847bd5acc75a')] ``` To list all prediction data exports, set the limit to `0`. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models.deployment import PredictionDataExport prediction_data_exports = PredictionDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=100, offset=100) # Use additional filters prediction_data_exports = PredictionDataExport.list( deployment_id='5c939e08962d741e34f609f0', model_id="6444482e5583f6ee2e572265", batch=False, status=ExportStatus.FAILED ) ``` ### Retrieve a prediction data export To get a prediction data export by identifier, use `PredictionDataExport.get`: ``` from datarobot.models.deployment import PredictionDataExport prediction_data_export = PredictionDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' ) prediction_data_exports >>> PredictionDataExport('65fbe59aaa3f847bd5acc75b') ``` ### Fetch prediction export datasets To return data from a prediction export as `dr.Dataset`, use the `fetch_data` method. This method can return a list of datasets; however, usually it returns one dataset. There are cases, like time series, when more than one element is returned. The obtained dataset (or datasets) can be transformed into, for example, a pandas DataFrame. ``` from datarobot.models.deployment import PredictionDataExport prediction_data_export = PredictionDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbe59aaa3f847bd5acc75b' ) prediction_datasets = prediction_data_export.fetch_data() prediction_datasets >>> [Dataset(name='Deployment prediction data', id='65f240b0e37a9f1a104bf450')] prediction_dataset = prediction_datasets[0] df = prediction_dataset.get_as_dataframe() df.head(2) >>> DR_RESERVED_PREDICTION_TIMESTAMP ... upstream_x_datarobot_version 0 2024-03-13 23:00:38.998000+00:00 ... predictionapi/X/X 1 2024-03-13 23:00:38.998000+00:00 ... predictionapi/X/X ``` ## Actuals data export The following examples outline how to manage actuals data exports. ### Create actuals data export To create an actuals data export, use `ActualsDataExport.create`, defining the time window to include in the export using the `start` and `end` parameters: ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', start=now - timedelta(days=7), end=now ) ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id="6444482e5583f6ee2e572265", start=now - timedelta(days=7), end=now, ) ``` To export only actuals that are matched to predictions, set `only_matched_predictions` to `True`; by default all available actuals are exported. The `start` and `end` of the export can be defined as a `datetime` or `string` type. ``` from datetime import datetime, timedelta from datarobot.models.deployment import ActualsDataExport now=datetime.now() actuals_data_export = ActualsDataExport.create( deployment_id='5c939e08962d741e34f609f0', only_matched_predictions=True, start=now - timedelta(days=7), end=now, ) ``` ### List actuals data exports To list actuals data exports, use `ActualsDataExport.list`: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_exports = ActualsDataExport.list(deployment_id='5c939e08962d741e34f609f0', limit=0) actuals_data_exports >>> [ActualsDataExport('660456a332d0081029ee5031'), ActualsDataExport('660456a332d0081029ee5032'), ActualsDataExport('660456a332d0081029ee5033')] ``` To list all actuals data exports, set the limit to `0`. Adjust additional parameters to filter the data as needed: ``` from datarobot.enums import ExportStatus from datarobot.models.deployment import ActualsDataExport # use additional filters actuals_data_exports = ActualsDataExport.list( deployment_id='5c939e08962d741e34f609f0', offset=500, limit=50, status=ExportStatus.SUCCEEDED ) ``` ### Retrieve actuals data export To get actuals data export by identifier, use `ActualsDataExport.get`, as in the following example: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_export = ActualsDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) actuals_data_export >>> ActualsDataExport('660456a332d0081029ee4031') ``` ### Fetch actuals export datasets To return data from actuals export as `dr.Dataset`, use the `fetch_data` method: ``` from datarobot.models.deployment import ActualsDataExport actuals_data_export = ActualsDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) actuals_datasets = actuals_data_export.fetch_data() actuals_datasets >>> [Dataset(name='Deployment prediction data', id='65f240b0e37a9f1a104bf450')] actuals_dataset = actuals_datasets[0] df = actuals_dataset.get_as_dataframe() df.head(2) >>> association_id timestamp actuals predictions 0 1 2024-03-20 15:00:00+00:00 21.0 18.125388 1 10 2024-03-20 15:00:00+00:00 12.0 22.805252 ``` This method may return a list of datasets; however, it usually returns one dataset. The obtained dataset (or datasets) can be transformed into, for example, a pandas DataFrame. ## Training data export The following examples outline how to manage training data exports. ### Create training data export To create a training data export, use `TrainingDataExport.create` and define the deployment ID: ``` from datarobot.models.deployment import TrainingDataExport dataset_id = TrainingDataExport.create(deployment_id='5c939e08962d741e34f609f0') ``` Specify the model ID for export. Otherwise, the champion model ID is used by default: ``` from datarobot.models.deployment import TrainingDataExport dataset_id = TrainingDataExport.create( deployment_id='5c939e08962d741e34f609f0', model_id='6444482e5583f6ee2e572265') dataset_id >>> 65fb0c25019ca3333bbb4c10 ``` This method returns the ID of the dataset that contains the training data. This dataset is saved in the AI Catalog. ### List training data exports To list training data exports, use `TrainingDataExport.list`: ``` from datarobot.models.deployment import TrainingDataExport training_data_exports = TrainingDataExport.list(deployment_id='5c939e08962d741e34f609f0') training_data_exports >>> [TrainingDataExport('6565fbf2356124f1daa3acc522')] ``` ### Retrieve a training data export To get training data export by identifier, use `TrainingDataExport.get`. ``` from datarobot.models.deployment import ActualsDataExport training_data_export = TrainingDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='65fbf2356124f1daa3acc522' ) training_data_export >>> TrainingDataExport('6565fbf2356124f1daa3acc522') ``` ### Fetch a training export dataset To return data from the training export as `dr.Dataset`, use `fetch_data`. This method returns a single training dataset. The obtained dataset can be transformed into, for example, a pandas DataFrame. ``` from datarobot.models.deployment import TrainingDataExport training_data_export = TrainingDataExport.get( deployment_id='5c939e08962d741e34f609f0', export_id='660456a332d0081029ee4031' ) training_dataset = training_data_export.fetch_data() training_dataset >>> [Dataset(name='training-data-10k_diabetes.csv', id='65fb0c25019ca3333bbb4c10')] df = training_dataset.get_as_dataframe() df.head(2) >>> acetohexamide time_in_hospital ... number_outpatient payer_code 0 No 1 ... 0 YY 1 No 2 ... 0 XX ``` ## Data quality export The data-quality exports provide feedback on LLM deployments. It is intended to be used in conjunction with custom-metrics for prompt monitoring. ### Data quality export list To list data quality exports, use `DataQualityExport.list`: The `start` and `end` of the export can be defined as a `datetime` or `string` type. There are many options for filtering and ordering the data. ``` from datetime import datetime, timedelta from datarobot.models.deployment import DataQualityExport now=datetime.now() data_quality_exports = DataQualityExport.list( deployment_id='66903c40f18e6ec90fd7c8c7', start=now - timedelta(days=1), end=now, ) data_quality_exports >>> [DataQualityExport(6447ca39c6a04df6b5b0ed19c6101e3c), ... DataQualityExport(0ff46fd3636545a9ac3e15ee1dbd8638)] data_quality_deports[0].metrics >>> [{'id': '669688f90a23524131e2d301', 'name': 'metric 3', 'value': None}, {'id': '669688e633ae1ffce40eb2f8', 'name': 'metric 2', 'value': 45.0}, {'id': '669688d282c9384ab8068a6c', 'name': 'metric 1', 'value': 178.0}] ```