Developer documentation > API reference > Python API client > Deployments > Deployment management

Deployment Management¶

Deployments¶

Deployment¶

class datarobot.models.Deployment

A deployment created from a DataRobot model.

Variables

Attribute	Type	Description
`id`	`str`	the ID of the deployment
`label`	`str`	the label of the deployment
`description`	`str`	the description of the deployment
`status`	`str`	(New in version v2.29) deployment status
`default_prediction_server`	`dict`	Information about the default prediction server for the deployment. Accepts the following keys: id (`str`) — Prediction server ID;
`url`	`Optional[str]`	Prediction server URL; datarobot-key (`str`) — Corresponds to the `PredictionServer` `snake_cased` `datarobot_key` parameter that allows you to verify and access the prediction server.
`importance`	`Optional[str]`	deployment importance
`model`	`dict`	information on the model of the deployment
`model_package`	`dict`	(New in version v3.4) information on the model package of the deployment
`prediction_usage`	`dict`	information on the prediction usage of the deployment
`permissions`	`list`	(New in version v2.18) user’s permissions on the deployment
`service_health`	`dict`	information on the service health of the deployment
`model_health`	`dict`	information on the model health of the deployment
`accuracy_health`	`dict`	information on the accuracy health of the deployment
`fairness_health`	`dict`	information on the fairness health of a deployment
`governance`	`dict`	information on approval and change requests of a deployment
`owners`	`dict`	information on the owners of a deployment
`prediction_environment`	`dict`	information on the prediction environment of a deployment
`creator`	`dict`	information about the creator of a deployment
`tags`	`list[dict[str,str]]`	Information about the deployment’s tags.

create_from_learning_model()¶

classmethod create_from_learning_model()

Create a deployment from a DataRobot model.

Added in version v2.17.

Parameters

Parameter	Type	Description
`model_id`	`str`	id of the DataRobot model to deploy
`label`	`str`	a human-readable label of the deployment
`description`	`Optional[str]`	a human-readable description of the deployment
`default_prediction_server_id`	`Optional[str]`	an identifier of a prediction server to be used as the default prediction server
`importance`	`Optional[str]`	deployment importance
`prediction_threshold`	`Optional[float]`	threshold used for binary classification in predictions
`status`	`Optional[str]`	deployment status
`max_wait`	`Optional[int]`	Seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished.

Returns

Returns	Description
deployment	The created deployment

Return type: Deployment

Examples

from datarobot import Project, Deployment
project = Project.get('5506fcd38bd88f5953219da0')
model = project.get_models()[0]
deployment = Deployment.create_from_learning_model(model.id, 'New Deployment')
deployment
>>> Deployment('New Deployment')

create_from_leaderboard()¶

classmethod create_from_leaderboard()

Create a deployment from a Leaderboard.

Added in version v2.17.

Parameters

Parameter	Type	Description
`model_id`	`str`	id of the Leaderboard to deploy
`label`	`str`	a human-readable label of the deployment
`description`	`Optional[str]`	a human-readable description of the deployment
`default_prediction_server_id`	`Optional[str]`	an identifier of a prediction server to be used as the default prediction server
`importance`	`Optional[str]`	deployment importance
`prediction_threshold`	`Optional[float]`	threshold used for binary classification in predictions
`status`	`Optional[str]`	deployment status
`max_wait`	`Optional[int]`	The amount of seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished.

Returns

Returns	Description
deployment	The created deployment

Return type: Deployment

Examples

from datarobot import Project, Deployment
project = Project.get('5506fcd38bd88f5953219da0')
model = project.get_models()[0]
deployment = Deployment.create_from_leaderboard(model.id, 'New Deployment')
deployment
>>> Deployment('New Deployment')

create_from_custom_model_version()¶

classmethod create_from_custom_model_version()

Create a deployment from a DataRobot custom model image.

Parameters

Parameter	Type	Description
`custom_model_version_id`	`str`	The ID of the DataRobot custom model version to deploy. The version must have a base_environment_id.
`label`	`str`	A label of the deployment.
`description`	`Optional[str]`	A description of the deployment.
`default_prediction_server_id`	`str`	An identifier of a prediction server to be used as the default prediction server. Required for SaaS users and optional for Self-Managed users.
`max_wait`	`Optional[int]`	Seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished.
`importance`	`Optional[str]`	Deployment importance level.

Returns

Returns	Description
deployment	The created deployment

Return type: Deployment

create_from_registered_model_version()¶

classmethod create_from_registered_model_version()

Create a deployment from a DataRobot model package (version).

Parameters

Parameter	Type	Description
`model_package_id`	`str`	The ID of the DataRobot model package (also known as a registered model version) to deploy.
`label`	`str`	A human readable label of the deployment.
`description`	`Optional[str]`	A human readable description of the deployment.
`default_prediction_server_id`	`Optional[str]`	an identifier of a prediction server to be used as the default prediction server When working with prediction environments, default prediction server Id should not be provided
`prediction_environment_id`	`Optional[str]`	An identifier of a prediction environment to be used for model deployment.
`importance`	`Optional[str]`	Deployment importance level.
`user_provided_id`	`Optional[str]`	A user-provided unique ID associated with a deployment definition in a remote git repository.
`additional_metadata`	`list[dict]`, optional	List of key/value dictionaries with additional metadata. For example, [{“key”: “Department”, “value”: “Engineering”}, {“key”: “Location”, “value”: “Boston”}]
`max_wait`	`Optional[int]`	The amount of seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished.

Returns

Returns	Description
deployment	The created deployment

Return type: Deployment

list()¶

classmethod list()

List all deployments a user can view.

Added in version v2.17.

Parameters

Parameter	Type	Description
`order_by`	`Optional[str]`	(New in version v2.18) the order to sort the deployment list by, defaults to label Allowed attributes to sort by are:- `label` - `serviceHealth` - `modelHealth` - `accuracyHealth` - `recentPredictions` - `lastPredictionTimestamp` If the sort attribute is preceded by a hyphen, deployments will be sorted in descending order, otherwise in ascending order. For health related sorting, ascending means failing, warning, passing, unknown.
`search`	`Optional[str]`	(New in version v2.18) case insensitive search against deployment’s label and description.
`filters`	`Optional[datarobot.models.deployment.DeploymentListFilters]`	(New in version v2.20) an object containing all filters that you’d like to apply to the resulting list of deployments. See `DeploymentListFilters` for details on usage.
`offset`	`Optional[int]`	The starting offset of the results. The default is 0.
`limit`	`Optional[int]`	The maximum number of objects to return. The default is 0 to maintain previous behavior. The default on the server is 20, with a maximum of 100.

Returns

Returns	Description
deployments	a list of deployments the user can view

Return type: list

Examples

from datarobot import Deployment
deployments = Deployment.list()
deployments
>>> [Deployment('New Deployment'), Deployment('Previous Deployment')]

from datarobot import Deployment
from datarobot.enums import DEPLOYMENT_SERVICE_HEALTH_STATUS
filters = DeploymentListFilters(
    role='OWNER',
    service_health=[DEPLOYMENT_SERVICE_HEALTH.FAILING]
)
filtered_deployments = Deployment.list(filters=filters)
filtered_deployments
>>> [Deployment('Deployment I Own w/ Failing Service Health')]

get()¶

classmethod get()

Get information about a deployment.

Added in version v2.17.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment

Returns

Returns	Description
deployment	the queried deployment

Return type: Deployment

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.id
>>>'5c939e08962d741e34f609f0'
deployment.label
>>>'New Deployment'

predict_batch()¶

method predict_batch()

A convenience method for making predictions with csv file or pandas DataFrame using a batch prediction job.

For advanced usage, use datarobot.models.BatchPredictionJob directly.

Added in version v3.0.

Parameters

Parameter	Type	Description
`source`	`str`, `pd.DataFrame` or `file object`	Pass a filepath, file, or DataFrame for making batch predictions.
`passthrough_columns`	`Optional[List[str]]`	Keep these columns from the scoring dataset in the scored dataset. This is useful for correlating predictions with source data.
`download_timeout`	`Optional[int]`	Wait this many seconds for the download to become available. See `datarobot.models.BatchPredictionJob.score()`.
`download_read_timeout`	`Optional[int]`	Wait this many seconds for the server to respond between chunks. See `datarobot.models.BatchPredictionJob.score()`.
`upload_read_timeout`	`Optional[int]`	Wait this many seconds for the server to respond after a whole dataset upload. See `datarobot.models.BatchPredictionJob.score()`.

Returns

Returns	Description
Prediction results in a pandas DataFrame.

Return type: pd.DataFrame

Raises

Exception	Description
InvalidUsageError	If the source parameter cannot be determined to be a filepath, file, or DataFrame.

Examples

from datarobot.models.deployment import Deployment

deployment = Deployment.get("<MY_DEPLOYMENT_ID>")
prediction_results_as_dataframe = deployment.predict_batch(
    source="./my_local_file.csv",
)

get_uri()¶

method get_uri()

Returns

Returns	Description
url	Deployment’s overview URI

Return type: str

update()¶

method update()

Update the label and description of this deployment.

Added in version v2.19.

Return type: None

delete()¶

method delete()

Delete this deployment.

Added in version v2.17.

Return type: None

activate()¶

method activate()

Activates this deployment. When succeeded, deployment status become active.

Added in version v2.29.

Parameters

Parameter	Type	Description
`max_wait`	`Optional[int]`	The maximum time to wait for deployment activation to complete before erroring

Return type: None

deactivate()¶

method deactivate()

Deactivates this deployment. When succeeded, deployment status become inactive.

Added in version v2.29.

Parameters

Parameter	Type	Description
`max_wait`	`Optional[int]`	The maximum time to wait for deployment deactivation to complete before erroring

Return type: None

replace_model()¶

method replace_model()

Replace the model used in this deployment. To confirm model replacement eligibility, use : validate_replacement_model() beforehand.

Added in version v2.17.

Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Predictions made against this deployment will start using the new model as soon as the request is completed. There will be no interruption for predictions throughout the process.

Parameters

Parameter	Type	Description
`new_model_id`	`Optional[str]`	The id of the new model to use. If replacing the deployment’s model with a CustomInferenceModel, a specific CustomModelVersion ID must be used. If None, new_registered_model_version_id must be specified.
`reason`	`MODEL_REPLACEMENT_REASON`	The reason for the model replacement. Must be one of ‘ACCURACY’, ‘DATA_DRIFT’, ‘ERRORS’, ‘SCHEDULED_REFRESH’, ‘SCORING_SPEED’, or ‘OTHER’. This value will be stored in the model history to keep track of why a model was replaced
`max_wait`	`Optional[int]`	(new in version 2.22) The maximum time to wait for model replacement job to complete before erroring
`new_registered_model_version_id`	`Optional[str]`	(new in version 3.4) The registered model version (model package) ID of the new model to use. Must be passed if new_model_id is None.

Return type: None

Examples

from datarobot import Deployment
from datarobot.enums import MODEL_REPLACEMENT_REASON
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.model['id'], deployment.model['type']
>>>('5c0a979859b00004ba52e431', 'Decision Tree Classifier (Gini)')

deployment.replace_model('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY)
deployment.model['id'], deployment.model['type']
>>>('5c0a969859b00004ba52e41b', 'Support Vector Classifier (Linear Kernel)')

perform_model_replace()¶

method perform_model_replace()

Replace the model used in this deployment. To confirm model replacement eligibility, use : validate_replacement_model() beforehand.

Added in version v3.4.

Model replacement is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Predictions made against this deployment will start using the new model as soon as the request is completed. There will be no interruption for predictions throughout the process.

Parameters

Parameter	Type	Description
`new_registered_model_version_id`	`str`	The registered model version (model package) ID of the new model to use.
`reason`	`MODEL_REPLACEMENT_REASON`	The reason for the model replacement. Must be one of ‘ACCURACY’, ‘DATA_DRIFT’, ‘ERRORS’, ‘SCHEDULED_REFRESH’, ‘SCORING_SPEED’, or ‘OTHER’. This value will be stored in the model history to keep track of why a model was replaced
`max_wait`	`Optional[int]`	The maximum time to wait for model replacement job to complete before erroring

Return type: None

Examples

from datarobot import Deployment
from datarobot.enums import MODEL_REPLACEMENT_REASON
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.model_package['id']
>>>'5c0a979859b00004ba52e431'

deployment.perform_model_replace('5c0a969859b00004ba52e41b', MODEL_REPLACEMENT_REASON.ACCURACY)
deployment.model_package['id']
>>>'5c0a969859b00004ba52e41b'

validate_replacement_model()¶

method validate_replacement_model()

Validate a model can be used as the replacement model of the deployment.

Added in version v2.17.

Parameters

Parameter	Type	Description
`new_model_id`	`Optional[str]`	the ID of the new model to validate
`new_registered_model_version_id`	`Optional[str]`	(new in version 3.4) The registered model version (model package) ID of the new model to use.

Return type: Tuple[str, str, Dict[str, Any]]

Returns

Returns	Description
status (`str`)	status of the validation, will be one of ‘passing’, ‘warning’ or ‘failing’. If the status is passing or warning, use `replace_model()` to perform a model replacement. If the status is failing, refer to `checks` for more detail on why the new model cannot be used as a replacement.
message (`str`)	message for the validation result
checks (`dict`)	explain why the new model can or cannot replace the deployment’s current model

get_features()¶

method get_features()

Retrieve the list of features needed to make predictions on this deployment.

Notes

Each feature dict contains the following structure:

name : str, feature name
feature_type : str, feature type
importance : float, numeric measure of the relationship strength between the feature and target (independent of model or other features)
date_format : str or None, the date format string for how this feature was interpreted, null if not a date feature, compatible with https://docs.python.org/2/library/time.html#time.strftime.
known_in_advance : bool, whether the feature was selected as known in advance in a time series model, false for non-time series models.

Returns

Returns	Description
features	a list of feature dict

Return type: list

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
features = deployment.get_features()
features[0]['feature_type']
>>>'Categorical'
features[0]['importance']
>>>0.133

submit_actuals()¶

method submit_actuals()

Submit actuals for processing. The actuals submitted will be used to calculate accuracy metrics.

Parameters

Parameter	Type	Description
`data`		(`list` or `pandas.DataFrame`)
`batch_size`		(`the max number` of `actuals in each request`)
`list`		(If data is a)
`and`		(each item should be a dict-like object with the following keys)
`pandas.DataFrame`		(values; if data is a)
`columns`		(it should contain the following)
`association_id`	`-`	max length 128 characters
`actual_value`	`-`	should be numeric for deployments with regression models or string for deployments with classification model
`was_acted_on`	`-`	could have affected the actual outcome
`timestamp`	`-`	does not have a timezone, we assume it is UTC.

Raises

Exception	Description
ValueError	if input data is not a list of dict-like objects or a pandas.DataFrame if input data is empty

Return type: None

Examples

from datarobot import Deployment, AccuracyOverTime
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
data = [{
    'association_id': '439917',
    'actual_value': 'True',
    'was_acted_on': True
}]
deployment.submit_actuals(data)

submit_actuals_from_catalog_async()¶

method submit_actuals_from_catalog_async()

Submit actuals from AI Catalog for processing. The actuals submitted will be used to calculate accuracy metrics.

Parameters

Parameter	Type	Description
`dataset_id`	`str,`	The ID of the source dataset.
`dataset_version_id`	`Optional[str]`	The ID of the dataset version to apply the query to. If not specified, the latest version associated with dataset_id is used.
`association_id_column`	`str,`	The name of the column that contains a unique identifier used with a prediction.
`actual_value_column`	`str,`	The name of the column that contains the actual value of a prediction.
`was_acted_on_column`	`Optional[str],`	The name of the column that indicates if the prediction was acted on in a way that could have affected the actual outcome.
`timestamp_column`	`Optional[str],`	The name of the column that contains datetime or string in RFC3339 format.

Returns

Returns	Description
status_check_job	Object contains all needed logic for a periodical status check of an async job.

Return type: StatusCheckJob

Raises

Exception	Description
ValueError	if dataset_id not provided if actual_value_column not provided if association_id_column not provided

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
status_check_job = deployment.submit_actuals_from_catalog_async(data)

get_predictions_by_forecast_date_settings()¶

method get_predictions_by_forecast_date_settings()

Retrieve predictions by forecast date settings of this deployment.

Added in version v2.27.

For time series deployments using the date/time format %Y-%m-%d %H:%M:%S.%f, DataRobot automatically populates a v2 in front of the timestamp format (forecast_date_format). Date/time values submitted in prediction data should not include this v2 prefix. Other timestamp formats are not affected.

Returns

Returns	Description
settings	Predictions by forecast date settings of the deployment.

Return type: ForecastDateSettings

update_predictions_by_forecast_date_settings()¶

method update_predictions_by_forecast_date_settings()

Update predictions by forecast date settings of this deployment.

Added in version v2.27.

Updating predictions by forecast date setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

For time series deployments using the date/time format %Y-%m-%d %H:%M:%S.%f, DataRobot automatically populates a v2 in front of the timestamp format (forecast_date_format). If you are updating predictions by forecast date settings for a Time Series deployment with date/time format %Y-%m-%d %H:%M:%S.%f, you must add the v2 prefix to your submitted forecast_date_format parameter. Date/time values submitted in prediction data should not include this v2 prefix. Other timestamp formats are not affected.

Examples

# To set predictions by forecast date settings to the same default settings you see when using
# the DataRobot web application, you use your 'Deployment' object like this:
deployment.update_predictions_by_forecast_date_settings(
   enable_predictions_by_forecast_date=True,
   forecast_date_column_name="date (actual)",
   forecast_date_format="%Y-%m-%d",
)

Parameters

Parameter	Type	Description
`enable_predictions_by_forecast_date`	`bool`	set to `True` if predictions by forecast date is to be turned on or set to ‘’False’’ if predictions by forecast date is to be turned off.
`forecast_date_column_name`	`string`, optional	The column name in prediction datasets to be used as forecast date. If ‘’enable_predictions_by_forecast_date’’ is set to ‘’False’’, then the parameter will be ignored.
`forecast_date_format`	`string`, optional	The datetime format of the forecast date column in prediction datasets. If ‘’enable_predictions_by_forecast_date’’ is set to ‘’False’’, then the parameter will be ignored.
`max_wait`	`Optional[int]`	seconds to wait for successful

Return type: None

get_challenger_models_settings()¶

method get_challenger_models_settings()

Retrieve challenger models settings of this deployment.

Added in version v2.27.

Returns

Returns	Description
settings

Return type: ChallengerModelsSettings

update_challenger_models_settings()¶

method update_challenger_models_settings()

Update challenger models settings of this deployment.

Added in version v2.27.

Updating challenger models setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters

Parameter	Type	Description
`challenger_models_enabled`	`bool`	set to `True` if challenger models is to be turned on or set to ‘’False’’ if challenger models is to be turned off
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_segment_analysis_settings()¶

method get_segment_analysis_settings()

Retrieve segment analysis settings of this deployment.

Added in version v2.27.

Returns

Returns	Description
settings

Return type: SegmentAnalysisSettings

update_segment_analysis_settings()¶

method update_segment_analysis_settings()

Update segment analysis settings of this deployment.

Added in version v2.27.

Updating segment analysis setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters

Parameter	Type	Description
`segment_analysis_enabled`	`bool`	set to `True` if segment analysis is to be turned on or set to ‘’False’’ if segment analysis is to be turned off
`segment_analysis_attributes`	`Optional[List]`	A list of strings that gives the segment attributes selected for tracking.
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_bias_and_fairness_settings()¶

method get_bias_and_fairness_settings()

Retrieve bias and fairness settings of this deployment.

..versionadded:: v3.2.0

Returns

Returns	Description
settings

Return type: BiasAndFairnessSettings

update_bias_and_fairness_settings()¶

method update_bias_and_fairness_settings()

Update bias and fairness settings of this deployment.

..versionadded:: v3.2.0

Updating bias and fairness setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters

Parameter	Type	Description
`protected_features`	`List[str]`	A list of features to mark as protected.
`preferable_target_value`	`bool`	A target value that should be treated as a positive outcome for the prediction.
`fairness_metric_set`	`str`	Can be one of . The fairness metric used to calculate the fairness scores.
`fairness_threshold`	`float`	Threshold value of the fairness metric. Cannot be less than 0 or greater than 1.
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_challenger_replay_settings()¶

method get_challenger_replay_settings()

Retrieve challenger replay settings of this deployment.

Added in version v3.4.

Returns

Returns	Description
settings

Return type: ChallengerReplaySettings

update_challenger_replay_settings()¶

method update_challenger_replay_settings()

Update challenger replay settings of this deployment.

Added in version v3.4.

Parameters

Parameter	Type	Description
`enabled`	`bool`	If challenger replay is enabled.
`schedule`	`Optional[Schedule]`	The recurring schedule for the challenger replay job.

Return type: None

get_drift_tracking_settings()¶

method get_drift_tracking_settings()

Retrieve drift tracking settings of this deployment.

Added in version v2.17.

Returns

Returns	Description
settings

Return type: DriftTrackingSettings

update_drift_tracking_settings()¶

method update_drift_tracking_settings()

Update drift tracking settings of this deployment.

Added in version v2.17.

Updating drift tracking setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters

Parameter	Type	Description
`target_drift_enabled`	`Optional[bool]`	if target drift tracking is to be turned on
`feature_drift_enabled`	`Optional[bool]`	if feature drift tracking is to be turned on
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_association_id_settings()¶

method get_association_id_settings()

Retrieve association ID setting for this deployment.

Added in version v2.19.

Returns

Returns	Description
association_id_settings

Return type: str

update_association_id_settings()¶

method update_association_id_settings()

Update association ID setting for this deployment.

Added in version v2.19.

Parameters

Parameter	Type	Description
`column_names`	`list[string]`, optional	name of the columns to be used as association ID, currently only support a list of one string
`required_in_prediction_requests`	`Optional[bool]`	whether the association ID column is required in prediction requests
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_predictions_data_collection_settings()¶

method get_predictions_data_collection_settings()

Retrieve predictions data collection settings of this deployment.

Added in version v2.21.

Returns

Returns	Description
predictions_data_collection_settings	- enabled (bool) : If predictions data collection is enabled for this deployment. To update existing ‘’predictions_data_collection’’ settings, see `update_predictions_data_collection_settings()`

Return type: dict in the following format:

update_predictions_data_collection_settings()¶

method update_predictions_data_collection_settings()

Update predictions data collection settings of this deployment.

Added in version v2.21.

Updating predictions data collection setting is an asynchronous process, which means some preparatory work may be performed after the initial request is completed. This function will not return until all preparatory work is fully finished.

Parameters

Parameter	Type	Description
`enabled`	`bool`	if predictions data collection is to be turned on
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_prediction_warning_settings()¶

method get_prediction_warning_settings()

Retrieve prediction warning settings of this deployment.

Added in version v2.19.

Returns

Returns	Description
settings

Return type: PredictionWarningSettings

update_prediction_warning_settings()¶

method update_prediction_warning_settings()

Update prediction warning settings of this deployment.

Added in version v2.19.

Parameters

Parameter	Type	Description
`prediction_warning_enabled`	`bool`	If prediction warnings should be turned on.
`use_default_boundaries`	`Optional[bool]`	If default boundaries of the model should be used for the deployment.
`upper_boundary`	`Optional[float]`	All predictions greater than provided value will be considered anomalous
`lower_boundary`	`Optional[float]`	All predictions less than provided value will be considered anomalous
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

get_prediction_intervals_settings()¶

method get_prediction_intervals_settings()

Retrieve prediction intervals settings for this deployment.

Added in version v2.19.

Notes

Note that prediction intervals are only supported for time series deployments.

Returns

Returns	Description
settings

Return type: PredictionIntervalsSettings

update_prediction_intervals_settings()¶

method update_prediction_intervals_settings()

Update prediction intervals settings for this deployment.

Added in version v2.19.

Notes

Updating prediction intervals settings is an asynchronous process, which means some preparatory work may be performed before the settings request is completed. This function will not return until all work is fully finished.

Note that prediction intervals are only supported for time series deployments.

Parameters

Parameter	Type	Description
`percentiles`	`list[int]`	The prediction intervals percentiles to enable for this deployment. Currently we only support setting one percentile at a time.
`enabled`	`Optional[bool] (defaults` to `True)`	Whether to enable showing prediction intervals in the results of predictions requested using this deployment.
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Raises

Exception	Description
AssertionError	If `percentiles` is in an invalid format
AsyncFailureError	If any of the responses from the server are unexpected
AsyncProcessUnsuccessfulError	If the prediction intervals calculation job has failed or has been cancelled.
AsyncTimeoutError	If the prediction intervals calculation job did not resolve in time

Return type: None

get_health_settings()¶

method get_health_settings()

Retrieve health settings of this deployment.

Added in version v3.4.

Returns

Returns	Description
settings

Return type: HealthSettings

update_health_settings()¶

method update_health_settings()

Update health settings of this deployment.

Added in version v3.4.

Parameters

Parameter	Type	Description
`service`	`dict`	Service health settings.
`data_drift`	`dict`	Data drift health settings.
`accuracy`	`dict`	Accuracy health settings.
`fairness`	`dict`	Fairness health settings.
`custom_metrics`	`dict`	Custom metrics health settings.
`predictions_timeliness`	`dict`	Predictions timeliness health settings.
`actuals_timeliness`	`dict`	Actuals timeliness health settings.

Return type: HealthSettings

get_default_health_settings()¶

method get_default_health_settings()

Retrieve default health settings of this deployment.

Added in version v3.4.

Returns

Returns	Description
settings

Return type: HealthSettings

get_service_stats()¶

method get_service_stats()

Retrieves values of many service stat metrics aggregated over a time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`model_id`	`Optional[str]`	the ID of the model
`start_time`	`datetime`, optional	start of the time period
`end_time`	`datetime`, optional	end of the time period
`execution_time_quantile`	`Optional[float]`	quantile for executionTime, defaults to 0.5
`response_time_quantile`	`Optional[float]`	quantile for responseTime, defaults to 0.5
`slow_requests_threshold`	`Optional[float]`	threshold for slowRequests, defaults to 1000
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
service_stats	the queried service stats metrics information

Return type: ServiceStats

get_service_stats_over_time()¶

method get_service_stats_over_time()

Retrieves values of a single service stat metric over a time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`metric`	`SERVICE_STAT_METRIC`, optional	the service stat metric to retrieve
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`, optional	start of the time period
`end_time`	`datetime`, optional	end of the time period
`bucket_size`	`Optional[str]`	time duration of a bucket, in ISO 8601 time duration format
`quantile`	`Optional[float]`	quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
`threshold`	`Optional[int]`	threshold for ‘slowQueries’, ignored when querying other metrics
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
service_stats_over_time	the queried service stats metric over time information

Return type: ServiceStatsOverTime

get_target_drift()¶

method get_target_drift()

Retrieve target drift information over a certain time period.

Added in version v2.21.

Parameters

Parameter	Type	Description
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`metric`	`str`	(New in version v2.22) metric used to calculate the drift score
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
target_drift	the queried target drift information

Return type: TargetDrift

get_feature_drift()¶

method get_feature_drift()

Retrieve drift information for deployment’s features over a certain time period.

Added in version v2.21.

Parameters

Parameter	Type	Description
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`metric`	`str`	(New in version v2.22) The metric used to calculate the drift score. Allowed values include psi, kl_divergence, dissimilarity, hellinger, and js_divergence.
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
feature_drift_data	the queried feature drift information

Return type: [FeatureDrift]

get_predictions_over_time()¶

method get_predictions_over_time()

Retrieve stats of deployment’s prediction response over a certain time period.

Added in version v3.2.

Parameters

Parameter	Type	Description
`model_ids`	`list[str]`	ID of models to retrieve prediction stats
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`BUCKET_SIZE`	time duration of each bucket
`target_classes`	`list[str]`	class names of target, only for deployments with multiclass target
`include_percentiles`	`bool`	if the returned data includes percentiles, only for a deployment with a binary and regression target
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
predictions_over_time	the queried predictions over time information

Return type: PredictionsOverTime

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
predictions_over_time = deployment.get_predictions_over_time()
predictions_over_time.buckets[0]['mean_predicted_value']
>>>0.3772
predictions_over_time.buckets[0]['row_count']
>>>2000

get_accuracy()¶

method get_accuracy()

Retrieves values of many accuracy metrics aggregated over a time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`target_classes`	`list[str]`, optional	Optional list of target class strings
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value
`metric`	`str`	(New in Version v3.9) the metric value to retrieve, must be provided when querying for multiple models
`baseline_model_id`	`str`	(New in Version v3.9) the ID of the baseline model when calculating percentage change

Returns

Returns	Description
accuracy	the queried accuracy metrics information

Return type: Accuracy

get_accuracy_over_time()¶

method get_accuracy_over_time()

Retrieves values of a single accuracy metric over a time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`metric`	`ACCURACY_METRIC`	the accuracy metric to retrieve
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`str`	time duration of a bucket, in ISO 8601 time duration format
`target_classes`	`list[str]`, optional	Optional list of target class strings
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
accuracy_over_time	the queried accuracy metric over time information

Return type: AccuracyOverTime

get_predictions_vs_actuals_over_time()¶

method get_predictions_vs_actuals_over_time()

Retrieve information for deployment’s predictions vs actuals over a certain time period.

Added in version v3.3.

Parameters

Parameter	Type	Description
`model_ids`	`list[str]`	The ID of models to retrieve predictions vs actuals stats for.
`start_time`	`datetime`	Start of the time period.
`end_time`	`datetime`	End of the time period.
`bucket_size`	`BUCKET_SIZE`	Time duration of each bucket.
`target_classes`	`list[str]`	Class names of target, only for deployments with a multiclass target.
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
predictions_vs_actuals_over_time	The queried predictions vs actuals over time information.

Return type: PredictionsVsActualsOverTime

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
predictions_over_time = deployment.get_predictions_vs_actuals_over_time()
predictions_over_time.buckets[0]['mean_actual_value']
>>>0.6673
predictions_over_time.buckets[0]['row_count_with_actual']
>>>500

get_fairness_scores_over_time()¶

method get_fairness_scores_over_time()

Retrieves values of a single fairness score over a time period.

Added in version v3.2.

Parameters

Parameter	Type	Description
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`str`	time duration of a bucket, in ISO 8601 time duration format
`protected_feature`	`str`	name of protected feature
`fairness_metric`	`str`	A consolidation of the fairness metrics by the use case.

Returns

Returns	Description
fairness_scores_over_time	the queried fairness score over time information

Return type: FairnessScoresOverTime

update_secondary_dataset_config()¶

method update_secondary_dataset_config()

Update the secondary dataset config used by Feature discovery model for a given deployment.

Added in version v2.23.

Parameters

Parameter	Type	Description
`secondary_dataset_config_id`	`str`	Id of the secondary dataset config
`credential_ids`	`list` or `None`	List of DatasetsCredentials used by the secondary datasets

Return type: str

Examples

from datarobot import Deployment
deployment = Deployment(deployment_id='5c939e08962d741e34f609f0')
config = deployment.update_secondary_dataset_config('5df109112ca582033ff44084')
config
>>> '5df109112ca582033ff44084'

get_secondary_dataset_config()¶

method get_secondary_dataset_config()

Get the secondary dataset config used by Feature discovery model for a given deployment.

Added in version v2.23.

Returns

Returns	Description
secondary_dataset_config	Id of the secondary dataset config

Return type: SecondaryDatasetConfigurations

Examples

from datarobot import Deployment
deployment = Deployment(deployment_id='5c939e08962d741e34f609f0')
deployment.update_secondary_dataset_config('5df109112ca582033ff44084')
config = deployment.get_secondary_dataset_config()
config
>>> '5df109112ca582033ff44084'

get_prediction_results()¶

method get_prediction_results()

Retrieve a list of prediction results of the deployment.

Added in version v2.24.

Parameters

Parameter	Type	Description
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`actuals_present`	`bool`	filters predictions results to only those who have actuals present or with missing actuals
`offset`	`int`	this many results will be skipped
`limit`	`int`	at most this many results are returned

Returns

Returns	Description
prediction_results	a list of prediction results

Return type: list[dict]

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.get_prediction_results()

download_prediction_results()¶

method download_prediction_results()

Download prediction results of the deployment as a CSV file.

Added in version v2.24.

Parameters

Parameter	Type	Description
`filepath`	`str`	path of the csv file
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`actuals_present`	`bool`	filters predictions results to only those who have actuals present or with missing actuals
`offset`	`int`	this many results will be skipped
`limit`	`int`	at most this many results are returned

Return type: None

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.download_prediction_results('path_to_prediction_results.csv')

download_scoring_code()¶

method download_scoring_code()

Retrieve scoring code of the current deployed model.

Added in version v2.24.

Notes

When setting include_agent or include_predictions_explanations or include_prediction_intervals to True, it can take a considerably longer time to download the scoring code.

Parameters

Parameter	Type	Description
`filepath`	`str`	path of the scoring code file
`source_code`	`bool`	whether source code or binary of the scoring code will be retrieved
`include_agent`	`bool`	whether the scoring code retrieved will include tracking agent
`include_prediction_explanations`	`bool`	whether the scoring code retrieved will include prediction explanations
`include_prediction_intervals`	`bool`	whether the scoring code retrieved will support prediction intervals
`max_wait`	`Optional[int]`	Seconds to wait for successful resolution of a deployment creation job. Deployment supports making predictions only after a deployment creating job has successfully finished

Return type: None

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
results = deployment.download_scoring_code('path_to_scoring_code.jar')

download_model_package_file()¶

method download_model_package_file()

Retrieve model package file (mlpkg) of the current deployed model.

Added in version v3.3.

Parameters

Parameter	Type	Description
`filepath`	`str`	The file path of the model package file.
`compute_all_ts_intervals`	`bool`	Includes all time series intervals into the built Model Package (.mlpkg) if set to True.

Return type: None

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.download_model_package_file('path_to_model_package.mlpkg')

delete_monitoring_data()¶

method delete_monitoring_data()

Delete deployment monitoring data.

Parameters

Parameter	Type	Description
`model_id`	`str`	id of the model to delete monitoring data
`start_time`	`datetime`, optional	start of the time period to delete monitoring data
`end_time`	`datetime`, optional	end of the time period to delete monitoring data
`max_wait`	`Optional[int]`	seconds to wait for successful resolution

Return type: None

list_shared_roles()¶

method list_shared_roles()

Get a list of users, groups and organizations that have an access to this user blueprint

Parameters

Parameter	Type	Description
`id`	`Optional[str]`	Only return the access control information for a organization, group or user with this ID.
`name`	`string`, `Optional`	Only return the access control information for a organization, group or user with this name.
`share_recipient_type`	enum(```’user’, 'group'`, `'organization')`,`Optional`	Only returns results with the given recipient type.
`limit`	`int (Default=0)`	At most this many results are returned.
`offset`	`int (Default=0)`	This many results will be skipped.

Return type: List[DeploymentSharedRole]

update_shared_roles()¶

method update_shared_roles()

Share a deployment with a user, group, or organization

Parameters

Parameter	Type	Description
`roles`	`list(or(GrantAccessControlWithUsernameValidator`, `GrantAccessControlWithIdValidator`, `SharingRole))`	Array of GrantAccessControl objects, up to maximum 100 objects.

Return type: None

method share()

Share a deployment with a user, group, or organization

Parameters

Parameter	Type	Description
`roles`	`list(SharingRole)`	Array of SharingRole objects.

Return type: None

list_challengers()¶

method list_challengers()

Get a list of challengers for this deployment.

Added in version v3.4.

Return type: list(Challenger)

get_agent_card()¶

method get_agent_card()

Retrieve the agent card for this deployment.

Returns

Returns	Description
agent_card	The agent card associated with this deployment.

Return type: dict

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
agent_card = deployment.get_agent_card()

upload_agent_card()¶

method upload_agent_card()

Upload or replace the agent card for this deployment.

This is only available for external deployments.

Parameters

Parameter	Type	Description
`agent_card`	`dict`	The agent card to upload for this deployment.

Returns

Returns	Description
agent_card	The uploaded agent card.

Return type: dict

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
agent_card = deployment.upload_agent_card({"name": "My Agent", "version": "1.0.0"})

delete_agent_card()¶

method delete_agent_card()

Delete the agent card for this deployment.

This is only available for external deployments. This operation is idempotent — returns successfully even if no agent card exists.

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.delete_agent_card()

Return type: None

get_champion_model_package()¶

method get_champion_model_package()

Get a champion model package for this deployment.

Returns

Returns	Description
champion_model_package	A champion model package object.

Return type: ChampionModelPackage

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
champion_model_package = deployment.get_champion_model_package()

list_prediction_data_exports()¶

method list_prediction_data_exports()

Retrieve a list of asynchronous prediction data exports.

Parameters

Parameter	Type	Description
`model_id`	`Optional[str]`	The ID of the model used for prediction data export.
`status`	`Optional[str]`	A prediction data export processing state.
`batch`	`Optional[bool]`	If true, only return batch exports. If false, only return real-time exports. If not provided, return both real-time and batch exports.
`limit`	`Optional[int]`	The maximum number of objects to return. The default is 100 (0 means no limit).
`offset`	`Optional[int]`	The starting offset of the results. The default is 0.

Returns

Returns	Description
prediction_data_exports	A list of prediction data exports.

Return type: List[PredictionDataExport]

list_actuals_data_exports()¶

method list_actuals_data_exports()

Retrieve a list of asynchronous actuals data exports.

Parameters

Parameter	Type	Description
`status`	`Optional[str]`	Actuals data export processing state.
`limit`	`Optional[int]`	The maximum number of objects to return. The default is 100 (0 means no limit).
`offset`	`Optional[int]`	The starting offset of the results. The default is 0.

Returns

Returns	Description
actuals_data_exports	A list of actuals data exports.

Return type: List[ActualsDataExport]

list_training_data_exports()¶

method list_training_data_exports()

Retrieve a list of successful training data exports.

Returns

Returns	Description
training_data_export	A list of training data exports.

Return type: List[TrainingDataExport]

list_data_quality_exports()¶

method list_data_quality_exports()

Retrieve a list of data-quality export records for a given deployment.

Added in version v3.6.

Parameters

Parameter	Type	Description
`start`	`Union[str`, `datetime]`	The earliest time of the objects to return.
`end`	`Union[str`, `datetime]`	The latest time of the objects to return.
`model_id`	`Optional[str]`	The ID of the model.
`prediction_pattern`	`Optional[str]`	The keywords to search in a predicted value for a text generation target.
`prompt_pattern`	`Optional[str]`	The keywords to search in a prompt value for a text generation target.
`actual_pattern`	`Optional[str]`	The keywords to search in an actual value for a text generation target.
`order_by`	`Optional[str]`	The field to sort by (e.g., associationId, timestamp, or customMetrics). Use a leading ‘-’ to indicate descending order. When ordering by a custom-metric, must also specify ‘order_metric’. The default is None, which equates to ‘-timestamp’.
`order_metric`	`Optional[str]`	When ‘order_by’ is a custom-metric, this specifies the custom-metric name or ID to use for ordering. The default is None.
`filter_metric`	`Optional[str]`	Specifies the metric name or ID to use for matching. Must also use ‘filter_value’ to specify the value that must be matched. The default is None.
`filter_value`	`Optional[str]`	Specifies the value associated with ‘filter_metric’ that must be matched. The default is None.
`offset`	`Optional[int]`	The starting offset of the results. The default is 0.
`limit`	`Optional[int]`	The maximum number of objects to return. The default is 100 (which is maximum).

Returns

Returns	Description
data_quality_exports	A list of DataQualityExport objects.

Return type: list

Examples

from datarobot import Deployment

deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
data_quality_exports = deployment.list_data_quality_exports(start_time='2024-07-01', end_time='2024-08-01')

get_capabilities()¶

method get_capabilities()

Get a list capabilities for this deployment.

Added in version v3.5.

Return type: list(Capability)

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
capabilities = deployment.get_capabilities()

get_segment_attributes()¶

method get_segment_attributes()

Get a list of segment attributes for this deployment.

Added in version v3.6.

Parameters

Parameter	Type	Description
`monitoringType`	`Optional[str]`	The monitoring type for which segment attributes are being retrieved.

Return type: list(str)

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
segment_attributes = deployment.get_segment_attributes(DEPLOYMENT_MONITORING_TYPE.SERVICE_HEALTH)

get_segment_values()¶

method get_segment_values()

Get a list of segment values for this deployment.

Added in version v3.6.

Parameters

Parameter	Type	Description
`segment_attribute`	`Optional[str]`	Represents the different ways that prediction requests can be viewed.
`limit`	`int`, `Optional`	The maximum number of values to return.
`offset`	`int`, `Optional`	The starting point of the values to be returned.
`search`	`Optional[str]`	A string to filter the values.

Return type: list(str)

Examples

from datarobot import Deployment
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
segment_values = deployment.get_segment_values(segment_attribute=ReservedSegmentAttributes.CONSUMER)

get_moderation_events()¶

method get_moderation_events()

Get a list of moderation events for this deployment

Parameters

Parameter	Type	Description
`limit`	`int (Default=100)`	The maximum number of values to return.
`offset`	`int (Default=0)`	The starting point of the values to be returned.

Returns

Returns	Description
events

Return type: List[MLOpsEvent]

get_accuracy_metrics_settings()¶

method get_accuracy_metrics_settings()

Get accuracy metrics settings for this deployment.

Returns

Returns	Description
accuracy_metrics	A list of deployment accuracy metric names.

Return type: list(str)

Examples

from datarobot import Deployment

deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
accuracy_metrics = deployment.get_accuracy_metrics_settings()

update_accuracy_metrics_settings()¶

method update_accuracy_metrics_settings()

Update accuracy metrics settings for this deployment.

Parameters

Parameter	Type	Description
`accuracy_metrics`	`list(str)`	A list of accuracy metric names.

Returns

Returns	Description
accuracy_metrics	A list of deployment accuracy metric names.

Return type: list(str)

Examples

from datarobot import Deployment
from datarobot.enums import ACCURACY_METRIC

deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
payload = [ACCURACY_METRIC.AUC, ACCURACY_METRIC.LOGLOSS]
accuracy_metrics = deployment.update_accuracy_metrics_settings(payload)

get_retraining_settings()¶

method get_retraining_settings()

Retrieve retraining settings of this deployment.

Added in version v2.29.

Returns

Returns	Description
settings

Return type: RetrainingSettings

Examples

from datarobot import Deployment

deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
retraining_settings = deployment.get_retraining_settings()

update_retraining_settings()¶

method update_retraining_settings()

Update retraining settings of this deployment.

Added in version v2.29.

Parameters

Parameter	Type	Description
`retraining_user_id`	`Optional[str]`	The retraining user ID.
`dataset_id`	`Optional[str]`	The retraining dataset ID.
`prediction_environment_id`	`Optional[str]`	The retraining prediction environment ID.

Return type: None

Examples

from datarobot import Deployment

deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
deployment.update_retraining_settings(retaining_user_id='5c939e08962d741e34f609f0')

create_tag()¶

method create_tag()

Create a new deployment tag.

Parameters

Parameter	Type	Description
`name`	`str`	The name of the deployment tag.
`value`	`str`	The value of the deployment tag.

Return type: dict[str, str]

update_tag()¶

method update_tag()

Update an existing deployment tag.

Parameters

Parameter	Type	Description
`id`	`str`	The ID of the deployment tag.
`name`	`str`	The new name of the deployment tag.
`value`	`str`	The new value of the deployment tag.

Return type: dict[str, str]

delete_tag()¶

method delete_tag()

Deletes the deployment tag specified by ID.

Parameters

Parameter	Type	Description
`id`	`str`	The ID of the deployment tag to delete.

Return type: None

from_data()¶

classmethod from_data()

Instantiate an object of this class using a dict.

Parameters

Parameter	Type	Description
`data`	`dict`	Correctly snake_cased keys and their values.

Return type: TypeVar(T, bound= APIObject)

from_server_data()¶

classmethod from_server_data()

Instantiate an object of this class using the data directly from the server, meaning that the keys may have the wrong camel casing

Parameters

Parameter	Type	Description
`data`	`dict`	The directly translated dict of JSON from the server. No casing fixes have taken place
`keep_attrs`	`iterable`	List, set or tuple of the dotted namespace notations for attributes to keep within the object structure even if their values are None

Return type: TypeVar(T, bound= APIObject)

open_in_browser()¶

method open_in_browser()

Opens class’ relevant web browser location. If default browser is not available the URL is logged.

Note: If text-mode browsers are used, the calling process will block until the user exits the browser.

Return type: None

DeploymentListFilters¶

class datarobot.models.deployment.DeploymentListFilters

Construct a set of filters to pass to Deployment.list()

Added in version v2.20.

Parameters

Parameter	Type	Description
`role`	`str`	A user role. If specified, then take those deployments that the user can view, then filter them down to those that the user has the specified role for, and return only them. Allowed options are `OWNER` and `USER`.
`service_health`	`List[str]`	A list of service health status values. If specified, then only deployments whose service health status is one of these will be returned. See `datarobot.enums.DEPLOYMENT_SERVICE_HEALTH_STATUS` for allowed values. Supports comma-separated lists.
`model_health`	`List[str]`	A list of model health status values. If specified, then only deployments whose model health status is one of these will be returned. See `datarobot.enums.DEPLOYMENT_MODEL_HEALTH_STATUS` for allowed values. Supports comma-separated lists.
`accuracy_health`	`List[str]`	A list of accuracy health status values. If specified, then only deployments whose accuracy health status is one of these will be returned. See `datarobot.enums.DEPLOYMENT_ACCURACY_HEALTH_STATUS` for allowed values. Supports comma-separated lists.
`execution_environment_type`	`List[str]`	A list of strings representing the type of the deployments’ execution environment. If provided, then only return those deployments whose execution environment type is one of those provided. See `datarobot.enums.DEPLOYMENT_EXECUTION_ENVIRONMENT_TYPE` for allowed values. Supports comma-separated lists.
`importance`	`List[str]`	A list of strings representing the deployments’ “importance”. If provided, then only return those deployments whose importance is one of those provided. See `datarobot.enums.DEPLOYMENT_IMPORTANCE` for allowed values. Supports comma-separated lists. Note that Approval Workflows must be enabled for your account to use this filter, otherwise the API will return a 403.
`tag_keys`	`List[str]`	List of tag keys to filter for. If multiple values are specified, deployments with tags that match any of the values will be returned. Supports comma-separated lists.
`tag_values`	`List[str]`	List of tag values to filter for. If multiple values are specified, deployments with tags that match any of the values will be returned. Supports comma-separated lists.
`is_a2a_agent`	`bool`	If specified, filters deployments based on whether they are A2A agents.”

Examples

Multiple filters can be combined in interesting ways to return very specific subsets of deployments.

Performing AND logic

Providing multiple different parameters will result in AND logic between them. For example, the following will return all deployments that I own whose service health status is failing.
from datarobot import Deployment
from datarobot.models.deployment import DeploymentListFilters
from datarobot.enums import DEPLOYMENT_SERVICE_HEALTH_STATUS
filters = DeploymentListFilters(
    role='OWNER',
    service_health=[DEPLOYMENT_SERVICE_HEALTH.FAILING]
)
deployments = Deployment.list(filters=filters)

Performing OR logic

Some filters support comma-separated lists (and will say so if they do). Providing a comma-separated list of values to a single filter performs OR logic between those values. For example, the following will return all deployments whose service health is either warning OR failing.
from datarobot import Deployment
from datarobot.models.deployment import DeploymentListFilters
from datarobot.enums import DEPLOYMENT_SERVICE_HEALTH_STATUS
filters = DeploymentListFilters(
    service_health=[
        DEPLOYMENT_SERVICE_HEALTH.WARNING,
        DEPLOYMENT_SERVICE_HEALTH.FAILING,
    ]
)
deployments = Deployment.list(filters=filters)

Performing OR logic across different filter types is not supported.

Notes

In all cases, you may only retrieve deployments for which you have at least the USER role for. Deployments for which you are a CONSUMER of will not be returned, regardless of the filters applied.

ServiceStats¶

class datarobot.models.deployment.ServiceStats

Deployment service stats information.

Variables

Attribute	Type	Description
`model_id`	`str`	the model used to retrieve service stats metrics
`period`	`dict`	the time period used to retrieve service stats metrics
`metrics`	`dict`	the service stats metrics

get()¶

classmethod get()

Retrieve value of service stat metrics over a certain time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_id`	`Optional[str]`	the ID of the model
`start_time`	`datetime`, optional	start of the time period
`end_time`	`datetime`, optional	end of the time period
`execution_time_quantile`	`Optional[float]`	quantile for executionTime, defaults to 0.5
`response_time_quantile`	`Optional[float]`	quantile for responseTime, defaults to 0.5
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value
`slow_requests_threshold`	`Optional[float]`	threshold for slowRequests, defaults to 1000

Returns

Returns	Description
service_stats	the queried service stats metrics

Return type: ServiceStats

ServiceStatsOverTime¶

class datarobot.models.deployment.ServiceStatsOverTime

Deployment service stats over time information.

Variables

Attribute	Type	Description
`metric`	`str`	the service stat metric being retrieved
`buckets`	`dict`	how the service stat metric changes over time

get()¶

classmethod get()

Retrieve information about how a service stat metric changes over a certain time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`metric`	`SERVICE_STAT_METRIC`, optional	the service stat metric to retrieve
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`, optional	start of the time period
`end_time`	`datetime`, optional	end of the time period
`bucket_size`	`Optional[str]`	time duration of a bucket, in ISO 8601 time duration format
`quantile`	`Optional[float]`	quantile for ‘executionTime’ or ‘responseTime’, ignored when querying other metrics
`threshold`	`Optional[int]`	threshold for ‘slowQueries’, ignored when querying other metrics
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
service_stats_over_time	the queried service stat over time information

Return type: ServiceStatsOverTime

bucket_values¶

property bucket_values

The metric value for all time buckets, keyed by start time of the bucket.

Returns

Returns	Description
bucket_values

Return type: OrderedDict

TargetDrift¶

class datarobot.models.deployment.TargetDrift

Deployment target drift information.

Variables

Attribute	Type	Description
`model_id`	`str`	the model used to retrieve target drift metric
`period`	`dict`	the time period used to retrieve target drift metric
`metric`	`str`	the data drift metric
`target_name`	`str`	name of the target
`drift_score`	`float`	target drift score
`sample_size`	`int`	count of data points for comparison
`baseline_sample_size`	`int`	count of data points for baseline

get()¶

classmethod get()

Retrieve target drift information over a certain time period.

Added in version v2.21.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`metric`	`str`	(New in version v2.22) metric used to calculate the drift score
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
target_drift	the queried target drift information

Return type: TargetDrift

Examples

from datarobot import Deployment, TargetDrift
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
target_drift = TargetDrift.get(deployment.id)
target_drift.period['end']
>>>'2019-08-01 00:00:00+00:00'
target_drift.drift_score
>>>0.03423
accuracy.target_name
>>>'readmitted'

FeatureDrift¶

class datarobot.models.deployment.FeatureDrift

Deployment feature drift information.

Variables

Attribute	Type	Description
`model_id`	`str`	the model used to retrieve feature drift metric
`period`	`dict`	the time period used to retrieve feature drift metric
`metric`	`str`	the data drift metric
`name`	`str`	name of the feature
`drift_score`	`float`	feature drift score
`sample_size`	`int`	count of data points for comparison
`baseline_sample_size`	`int`	count of data points for baseline

list()¶

classmethod list()

Retrieve drift information for deployment’s features over a certain time period.

Added in version v2.21.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_id`	`str`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`metric`	`str`	(New in version v2.22) metric used to calculate the drift score
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
feature_drift_data	the queried feature drift information

Return type: [FeatureDrift]

Examples

from datarobot import Deployment, TargetDrift
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
feature_drift = FeatureDrift.list(deployment.id)[0]
feature_drift.period
>>>'2019-08-01 00:00:00+00:00'
feature_drift.drift_score
>>>0.252
feature_drift.name
>>>'age'

PredictionsOverTime¶

class datarobot.models.deployment.PredictionsOverTime

Deployment predictions over time information.

Variables

Attribute	Type	Description
`baselines`	`List`	target baseline for each model queried
`buckets`	`List`	predictions over time bucket for each model and bucket queried

get()¶

classmethod get()

Retrieve information for deployment’s prediction response over a certain time period.

Added in version v3.2.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_ids`	`list[str]`	ID of models to retrieve prediction stats
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`BUCKET_SIZE`	time duration of each bucket
`target_classes`	`list[str]`	class names of target, only for deployments with multiclass target
`include_percentiles`	`bool`	if the returned data includes percentiles, only for a deployment with a binary and regression target
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
predictions_over_time	the queried predictions over time information

Return type: PredictionsOverTime

Accuracy¶

class datarobot.models.deployment.Accuracy

Deployment accuracy information.

Variables

Attribute	Type	Description
`model_id`	`str`	the model used to retrieve accuracy metrics
`period`	`dict`	the time period used to retrieve accuracy metrics
`metrics`	`dict`	the accuracy metrics

get()¶

classmethod get()

Retrieve values of accuracy metrics over a certain time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`target_classes`	`list[str]`, optional	Optional list of target class strings
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value
`metric`	`str`	(New in Version v3.9) the metric value to retrieve, must be provided when querying for multiple models
`baseline_model_id`	`str`	(New in Version v3.9) the ID of the baseline model when calculating percentage change

Returns

Returns	Description
accuracy	the queried accuracy metrics information

Return type: Accuracy

Examples

from datarobot import Deployment, Accuracy
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
accuracy = Accuracy.get(deployment.id)
accuracy.period['end']
>>>'2019-08-01 00:00:00+00:00'
accuracy.metric['LogLoss']['value']
>>>0.7533
accuracy.metric_values['LogLoss']
>>>0.7533

metric_values¶

property metric_values

The value for all metrics, keyed by metric name.

Returns

Returns	Description
metric_values

Return type: Dict

metric_baselines¶

property metric_baselines

The baseline value for all metrics, keyed by metric name.

Returns

Returns	Description
metric_baselines

Return type: Dict

percent_changes¶

property percent_changes

The percent change of value over baseline for all metrics, keyed by metric name.

Returns

Returns	Description
percent_changes

Return type: Dict

AccuracyOverTime¶

class datarobot.models.deployment.AccuracyOverTime

Deployment accuracy over time information.

Variables

Attribute	Type	Description
`metric`	`str`	the accuracy metric being retrieved
`buckets`	`list`	how the accuracy metric changes over time
`baselines`	`list`	baselines for the accuracy metric

get()¶

classmethod get()

Retrieve information about how an accuracy metric changes over a certain time period.

Added in version v2.18.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`metric`	`ACCURACY_METRIC`	the accuracy metric to retrieve
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`str`	time duration of a bucket, in ISO 8601 time duration format
`target_classes`	`list[str]`, optional	Optional list of target class strings
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
accuracy_over_time	the queried accuracy metric over time information

Return type: AccuracyOverTime

Examples

from datarobot import Deployment, AccuracyOverTime
from datarobot.enums import ACCURACY_METRICS
deployment = Deployment.get(deployment_id='5c939e08962d741e34f609f0')
accuracy_over_time = AccuracyOverTime.get(deployment.id, metric=ACCURACY_METRIC.LOGLOSS)
accuracy_over_time.metric
>>>'LogLoss'
accuracy_over_time.metric_values
>>>{datetime.datetime(2019, 8, 1): 0.73, datetime.datetime(2019, 8, 2): 0.55}

get_as_dataframe()¶

classmethod get_as_dataframe()

Retrieve information about how a list of accuracy metrics change over a certain time period as pandas DataFrame.

In the returned DataFrame, the columns corresponds to the metrics being retrieved; the rows are labeled with the start time of each bucket.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`metrics`	`[ACCURACY_METRIC]`	the accuracy metrics to retrieve
`model_id`	`Optional[str \\| List[str]]`	the ID of the model
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`str`	time duration of a bucket, in ISO 8601 time duration format

Returns

Returns	Description
accuracy_over_time

Return type: pd.DataFrame

bucket_values¶

property bucket_values

The metric value for all time buckets, keyed by start time of the bucket.

Returns

Returns	Description
bucket_values

Return type: Dict

bucket_sample_sizes¶

property bucket_sample_sizes

The sample size for all time buckets, keyed by start time of the bucket.

Returns

Returns	Description
bucket_sample_sizes

Return type: Dict

PredictionsVsActualsOverTime¶

class datarobot.models.deployment.PredictionsVsActualsOverTime

Deployment predictions vs actuals over time information.

Variables

Attribute	Type	Description
`summary`	`dict`	predictions vs actuals over time summary for all models and buckets queried
`baselines`	`List`	target baseline for each model queried
`buckets`	`List`	predictions vs actuals over time bucket for each model and bucket queried
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

get()¶

classmethod get()

Retrieve information for deployment’s predictions vs actuals over a certain time period.

Added in version v3.3.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_ids`	`list[str]`	ID of models to retrieve predictions vs actuals stats
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`bucket_size`	`BUCKET_SIZE`	time duration of each bucket
`target_classes`	`list[str]`	class names of target, only for deployments with multiclass target
`segment_attribute`	`Optional[str]`	(New in Version v3.6) the segment attribute
`segment_value`	`Optional[str]`	(New in Version v3.6) the segment value

Returns

Returns	Description
predictions_vs_actuals_over_time	the queried predictions vs actuals over time information

Return type: PredictionsVsActualsOverTime

FairnessScoresOverTime¶

class datarobot.models.deployment.bias_and_fairness.FairnessScoresOverTime

Deployment fairness over time information.

Variables

Attribute	Type	Description
`buckets`	`List`	fairness over time bucket for each model and bucket queried
`summary`	`dict`	summary for the fairness score
`protected_feature`	`str`	name of protected feature
`fairnessThreshold`	`float`	threshold used to compute fairness results
`modelId`	`str`	model id for which fairness is computed
`modelPackageId`	`str`	model package (version) id for which fairness is computed
`favorableTargetOutcome`	`bool`	preferable class of the target

get()¶

classmethod get()

Retrieve information for deployment’s fairness score response over a certain time period.

Added in version FUTURE.

Parameters

Parameter	Type	Description
`deployment_id`	`str`	the ID of the deployment
`model_id`	`str`	id of models to retrieve fairness score stats
`start_time`	`datetime`	start of the time period
`end_time`	`datetime`	end of the time period
`protected_feature`	`str`	name of the protected feature
`fairness_metric`	`str`	A consolidation of the fairness metrics by the use case.
`bucket_size`	`BUCKET_SIZE`	time duration of each bucket

Returns

Returns	Description
fairness_scores_over_time	the queried fairness score over time information

Return type: FairnessScoresOverTime

DeploymentSharedRole¶

class datarobot.models.deployment.DeploymentSharedRole

Parameters

Parameter	Type	Description
`share_recipient_type`	`{'user', 'group', 'organization'}`	Describes the recipient type, either user, group, or organization.
`role`	`{'CONSUMER', 'USER', 'OWNER'}`	The role of the org/group/user on this deployment.
`id`	`str`	The ID of the recipient organization, group, or user.
`name`	`str`	The name of the recipient organization, group, or user.

DeploymentGrantSharedRoleWithId¶

class datarobot.models.deployment.DeploymentGrantSharedRoleWithId

Parameters

Parameter	Type	Description
`share_recipient_type`	`{'user', 'group', 'organization'}`	Describes the recipient type, either user, group, or organization.
`role`	`{'OWNER', 'USER', 'OBSERVER', 'NO_ROLE'}`	The role of the recipient on this entity. One of OWNER, USER, OBSERVER, NO_ROLE. If NO_ROLE is specified, any existing role for the recipient will be removed.
`id`	`str`	The ID of the recipient.

DeploymentGrantSharedRoleWithUsername¶

class datarobot.models.deployment.DeploymentGrantSharedRoleWithUsername

Parameters

Parameter	Type	Description
`role`	`string`	The role of the recipient on this entity. One of OWNER, USER, CONSUMER, NO_ROLE. If NO_ROLE is specified, any existing role for the user will be removed.
`username`	`string`	Username of the user to update the access role for.

FeatureDict¶

class datarobot.models.deployment.deployment.FeatureDict

ForecastDateSettings¶

class datarobot.models.deployment.deployment.ForecastDateSettings

Forecast date settings of the deployment

Variables

Attribute	Type	Description
`enabled`	`bool`	Is `True` if predictions by forecast date is enabled for this deployment. To update this setting, see `update_predictions_by_forecast_date_settings()`
`column_name`	`str`	The column name in prediction datasets to be used as forecast date.
`datetime_format`	`str`	The datetime format of the forecast date column in prediction datasets.

ChallengerModelsSettings¶

class datarobot.models.deployment.deployment.ChallengerModelsSettings

Challenger models settings of the deployment is a dict with the following format:

Variables

Attribute	Type	Description
`enabled`	`bool`	Is `True` if challenger models is enabled for this deployment. To update existing ‘’challenger_models’’ settings, see `update_challenger_models_settings()`

SegmentAnalysisSettings¶

class datarobot.models.deployment.deployment.SegmentAnalysisSettings

Segment analysis settings of the deployment containing two items with keys : enabled and attributes, which are further described below.

Variables

Attribute	Type	Description
`enabled`	`bool`	Set to `True` if segment analysis is enabled for this deployment. To update existing setting, see `update_segment_analysis_settings()`
`attributes`	`list`	To create or update existing segment analysis attributes, see `update_segment_analysis_settings()`

BiasAndFairnessSettings¶

class datarobot.models.deployment.deployment.BiasAndFairnessSettings

Bias and fairness settings of this deployment

Variables

Attribute	Type	Description
`protected_features`	`List[str]`	A list of features to mark as protected.
`preferable_target_value`	`bool`	A target value that should be treated as a positive outcome for the prediction.
`fairness_metric_set`	`str`	Can be one of . A set of fairness metrics to use for calculating fairness.
`fairness_threshold`	`float`	Threshold value of the fairness metric. Cannot be less than 0 or greater than 1.

ChallengerReplaySettings¶

class datarobot.models.deployment.deployment.ChallengerReplaySettings

Challenger replay settings of the deployment is a dict with the following format:

Variables

Attribute	Type	Description
`enabled`	`bool`	If challenger replay is enabled. To update existing `challenger_replay` settings, see `update_challenger_replay_settings()`
`schedule`	`Schedule`	The recurring schedule for the challenger replay job.

HealthSettings¶

class datarobot.models.deployment.deployment.HealthSettings

Health settings of the deployment containing seven nested dicts with keys

Variables

Attribute	Type	Description
`service`	`dict`	Service health settings.
`data_drift`	`dict`	Data drift health settings.
`accuracy`	`dict`	Accuracy health settings.
`fairness`	`dict`	Fairness health settings.
`custom_metrics`	`dict`	Custom metrics health settings.
`predictions_timeliness`	`dict`	Predictions timeliness health settings.
`actuals_timeliness`	`dict`	Actuals timeliness health settings.

DriftTrackingSettings¶

class datarobot.models.deployment.deployment.DriftTrackingSettings

Drift tracking settings of the deployment containing two nested dicts with key : target_drift and feature_drift, which are further described below.

Variables

Attribute	Type	Description
`target_drift`	`Settings`	If target drift tracking is enabled for this deployment. To create or update existing `target_drift` settings, see `update_drift_tracking_settings()`
`feature_drift`	`Settings`	If feature drift tracking is enabled for this deployment. To create or update existing `feature_drift` settings, see `update_drift_tracking_settings()`

PredictionWarningSettings¶

class datarobot.models.deployment.deployment.PredictionWarningSettings

Prediction warning settings of the deployment

Variables

Attribute	Type	Description
`enabled`	`bool`	If target prediction_warning is enabled for this deployment. To create or update existing ‘’prediction_warning’’ settings, see `update_prediction_warning_settings()`
`custom_boundaries`	`dict` or `None`	If None default boundaries for a model are used. Otherwise has following keys:upper (float): All predictions greater than provided value are considered anomalous lower (float): All predictions less than provided value are considered anomalous

PredictionIntervalsSettings¶

class datarobot.models.deployment.deployment.PredictionIntervalsSettings

Prediction intervals settings of the deployment is a dict with the following format:

Variables

Attribute	Type	Description
`enabled`	`bool`	Whether prediction intervals are enabled for this deployment
`percentiles`	`list[int]`	List of enabled prediction intervals’ sizes for this deployment. Currently we only support one percentile at a time.

Capability¶

class datarobot.models.deployment.deployment.Capability

ACCURACY_METRIC¶

class datarobot.enums.ACCURACY_METRIC

Predictions¶

class datarobot.models.Predictions

Represents predictions metadata and provides access to prediction results.

Variables

Attribute	Type	Description
`project_id`	`str`	id of the project the model belongs to
`model_id`	`str`	id of the model
`prediction_id`	`str`	id of generated predictions
`includes_prediction_intervals`	`Optional[bool]`	(New in v2.16) For time series projects only. Indicates if prediction intervals will be part of the response. Defaults to False.
`prediction_intervals_size`	`Optional[int]`	(New in v2.16) For time series projects only. Indicates the percentile used for prediction intervals calculation. Will be present only if includes_prediction_intervals is True.
`forecast_point`	`datetime.datetime`, optional	(New in v2.20) For time series projects only. This is the default point relative to which predictions will be generated, based on the forecast window of the project. See the time series prediction documentation for more information.
`predictions_start_date`	`datetime.datetime` or `None`, optional	(New in v2.20) For time series projects only. The start date for bulk predictions. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with `predictions_end_date`. Can’t be provided with the `forecast_point` parameter.
`predictions_end_date`	`datetime.datetime` or `None`, optional	(New in v2.20) For time series projects only. The end date for bulk predictions, exclusive. Note that this parameter is for generating historical predictions using the training data. This parameter should be provided in conjunction with `predictions_start_date`. Can’t be provided with the `forecast_point` parameter.
`actual_value_column`	`string`, optional	(New in version v2.21) For time series unsupervised projects only. Actual value column which was used to calculate the classification metrics and insights on the prediction dataset. Can’t be provided with the `forecast_point` parameter.
`explanation_algorithm`	`datarobot.enums.EXPLANATIONS_ALGORITHM`, optional	(New in version v2.21) If set to ‘shap’, the response will include prediction explanations based on the SHAP explainer (SHapley Additive exPlanations). Defaults to null (no prediction explanations).
`max_explanations`	`Optional[int]`	(New in version v2.21) The maximum number of explanation values that should be returned for each row, ordered by absolute value, greatest to least. If null, no limit. In the case of ‘shap’: if the number of features is greater than the limit, the sum of remaining values will also be returned as shapRemainingTotal. Defaults to null. Cannot be set if explanation_algorithm is omitted.
`shap_warnings`	`dict`, optional	(New in version v2.21) Will be present if explanation_algorithm was set to datarobot.enums.EXPLANATIONS_ALGORITHM.SHAP and there were additivity failures during SHAP values calculation.

Examples

List all predictions for a project

import datarobot as dr

# Fetch all predictions for a project
all_predictions = dr.Predictions.list(project_id)

# Inspect all calculated predictions
for predictions in all_predictions:
    print(predictions)  # repr includes project_id, model_id, and dataset_id

Retrieve predictions by id

import datarobot as dr

# Getting predictions by id
predictions = dr.Predictions.get(project_id, prediction_id)

# Dump actual predictions
df = predictions.get_all_as_dataframe()
print(df)

list()¶

classmethod list()

Fetch all the computed predictions metadata for a project.

Parameters

Parameter	Type	Description
`project_id`	`str`	id of the project
`model_id`	`Optional[str]`	if specified, only predictions metadata for this model will be retrieved
`dataset_id`	`Optional[str]`	if specified, only predictions metadata for this dataset will be retrieved

Return type: A list of Predictions objects

get()¶

classmethod get()

Retrieve the specific predictions metadata

Parameters

Parameter	Type	Description
`project_id`	`str`	id of the project the model belongs to
`prediction_id`	`str`	id of the prediction set

Return type: Predictions

Returns

Returns	Description
* `Predictions` object representing specified
* `predictions`

get_all_as_dataframe()¶

method get_all_as_dataframe()

Retrieve all prediction rows and return them as a pandas.DataFrame.

Parameters

Parameter	Type	Description
`class_prefix`	`Optional[str]`	The prefix to append to labels in the final dataframe. Default is `class_` (e.g., apple -> class_apple)
`serializer`	`Optional[str]`	Serializer to use for the download. Options: `json` (default) or `csv`.

Returns

Returns	Description
dataframe

Return type: pandas.DataFrame

Raises

Exception	Description
datarobot.errors.ClientError	if the server responded with 4xx status.
datarobot.errors.ServerError	if the server responded with 5xx status.

download_to_csv()¶

method download_to_csv()

Save prediction rows into CSV file.

Parameters

Parameter	Type	Description
`filename`	`str` or `file object`	path or file object to save prediction rows
`encoding`	`string`, optional	A string representing the encoding to use in the output file, defaults to ‘utf-8’
`serializer`	`Optional[str]`	Serializer to use for the download. Options: `json` (default) or `csv`.

Return type: None

PredictionServer¶

class datarobot.PredictionServer

A prediction server can be used to make predictions.

Variables

Attribute	Type	Description
`id`	`Optional[str]`	The id of the prediction server.
`url`	`str`	The url of the prediction server.
`datarobot_key`	`Optional[str]`	The `Datarobot-Key` HTTP header used in requests to this prediction server. Note that in the `datarobot.models.Deployment` instance there is the `default_prediction_server` property which has this value as a “kebab-cased” key as opposed to “snake_cased”.

list()¶

classmethod list()

Returns a list of prediction servers a user can use to make predictions.

Added in version v2.17.

Returns

Returns	Description
prediction_servers	Contains a list of prediction servers that can be used to make predictions.

Return type: list of PredictionServer instances

Examples

prediction_servers = PredictionServer.list()
prediction_servers
>>> [PredictionServer('https://example.com')]

Prediction environment¶

PredictionEnvironment¶

class datarobot.models.PredictionEnvironment

A prediction environment entity.

Added in version v3.3.0.

Variables

Attribute	Type	Description
`id`	`str`	The ID of the prediction environment.
`name`	`str`	The name of the prediction environment.
`description`	`Optional[str]`	The description of the prediction environment.
`platform`	`Optional[str]`	Indicates which platform is in use (AWS, GCP, DataRobot, etc.).
`permissions`	`Optional[List]`	A set of permissions for the prediction environment.
`is_deleted`	`boolean`, optional	The flag that shows if this prediction environment deleted.
`supported_model_formats`	`list[PredictionEnvironmentModelFormats]`, optional	The list of supported model formats.
`is_managed_by_management_agent`	`boolean`, optional	Determines if the prediction environment should be managed by the management agent. False by default.
`datastore_id`	`Optional[str]`	The ID of the data store connection configuration. Only applicable for external prediction environments managed by DataRobot.
`credential_id`	`Optional[str]`	The ID of the credential associated with the data connection. Only applicable for external prediction environments managed by DataRobot.

list()¶

classmethod list()

Returns list of available external prediction environments.

Returns

Returns	Description
prediction_environments	contains a list of available prediction environments.

Return type: list of PredictionEnvironment instances

Examples

>>> import datarobot as dr
>>> prediction_environments = dr.PredictionEnvironment.list()
>>> prediction_environments
[
    PredictionEnvironment('5e429d6ecf8a5f36c5693e03', 'demo_pe', 'aws', 'env for demo testing'),
    PredictionEnvironment('5e42cc4dcf8a5f3256865840', 'azure_pe', 'azure', 'env for azure demo testing'),
]

get()¶

classmethod get()

Gets the PredictionEnvironment by id.

Parameters

Parameter	Type	Description
`pe_id`	`str`	the identifier of the PredictionEnvironment.

Returns

Returns	Description
prediction_environment	the requested prediction environment object.

Return type: PredictionEnvironment

Examples

>>> import datarobot as dr
>>> pe = dr.PredictionEnvironment.get('5a8ac9ab07a57a1231be501f')
>>> pe
PredictionEnvironment('5a8ac9ab07a57a1231be501f', 'my_predict_env', 'aws', 'demo env'),

delete()¶

method delete()

Deletes the prediction environment.

Examples

>>> import datarobot as dr
>>> pe = dr.PredictionEnvironment.get('5a8ac9ab07a57a1231be501f')
>>> pe.delete()

Return type: None

create()¶

classmethod create()

Create a prediction environment.

Parameters

Parameter	Type	Description
`name`	`str`	The name of the prediction environment.
`description`	`Optional[str]`	The description of the prediction environment.
`platform`	`str`	Indicates which platform is in use (AWS, GCP, DataRobot, etc.).
`plugin`	`str`	Optional. The plugin name to use.
`supported_model_formats`	`list[PredictionEnvironmentModelFormats]`, optional	The list of supported model formats. When not provided, the default value is inferred based on platform, (DataRobot platform: DataRobot, Custom Models; All other platforms: DataRobot, Custom Models, External Models).
`is_managed_by_management_agent`	`boolean`, optional	Determines if this prediction environment should be managed by the management agent. default: False
`datastore`	`DataStore\\|Optional[str]]`	The datastore object or ID of the data store connection configuration. Only applicable for external Prediction Environments managed by DataRobot.
`credential`	`Credential\\|Optional[str]]`	The credential object or ID of the credential associated with the data connection. Only applicable for external Prediction Environments managed by DataRobot.

Returns

Returns	Description
prediction_environment	the prediction environment was created

Return type: PredictionEnvironment

Raises

Exception	Description
datarobot.errors.ClientError	If the server responded with 4xx status.
datarobot.errors.ServerError	If the server responded with 5xx status.

Examples

>>> import datarobot as dr
>>> pe = dr.PredictionEnvironment.create(
...     name='my_predict_env',
...     platform=PredictionEnvironmentPlatform.AWS,
...     description='demo prediction env',
... )
>>> pe
PredictionEnvironment('5e429d6ecf8a5f36c5693e99', 'my_predict_env', 'aws', 'demo prediction env'),

Deployment Management¶

Deployments¶

Deployment¶

create_from_learning_model()¶

create_from_leaderboard()¶

create_from_custom_model_version()¶

create_from_registered_model_version()¶

list()¶

get()¶

predict_batch()¶

get_uri()¶

update()¶

delete()¶

activate()¶

deactivate()¶

replace_model()¶

perform_model_replace()¶

validate_replacement_model()¶

get_features()¶

submit_actuals()¶

submit_actuals_from_catalog_async()¶

get_predictions_by_forecast_date_settings()¶

update_predictions_by_forecast_date_settings()¶

get_challenger_models_settings()¶

update_challenger_models_settings()¶

get_segment_analysis_settings()¶

update_segment_analysis_settings()¶

get_bias_and_fairness_settings()¶

update_bias_and_fairness_settings()¶

get_challenger_replay_settings()¶

update_challenger_replay_settings()¶

get_drift_tracking_settings()¶

update_drift_tracking_settings()¶

get_association_id_settings()¶

update_association_id_settings()¶

get_predictions_data_collection_settings()¶

SEE ALSO¶

update_predictions_data_collection_settings()¶

get_prediction_warning_settings()¶

update_prediction_warning_settings()¶

get_prediction_intervals_settings()¶

update_prediction_intervals_settings()¶

get_health_settings()¶

update_health_settings()¶

get_default_health_settings()¶

get_service_stats()¶

get_service_stats_over_time()¶

get_target_drift()¶

get_feature_drift()¶

get_predictions_over_time()¶

get_accuracy()¶

get_accuracy_over_time()¶

get_predictions_vs_actuals_over_time()¶

get_fairness_scores_over_time()¶

update_secondary_dataset_config()¶

get_secondary_dataset_config()¶

get_prediction_results()¶

download_prediction_results()¶

download_scoring_code()¶

download_model_package_file()¶

delete_monitoring_data()¶

list_shared_roles()¶

update_shared_roles()¶

share()¶

list_challengers()¶

get_agent_card()¶

upload_agent_card()¶

delete_agent_card()¶

get_champion_model_package()¶

list_prediction_data_exports()¶

list_actuals_data_exports()¶

list_training_data_exports()¶

list_data_quality_exports()¶

get_capabilities()¶

get_segment_attributes()¶

get_segment_values()¶

get_moderation_events()¶

get_accuracy_metrics_settings()¶

update_accuracy_metrics_settings()¶

get_retraining_settings()¶