Deployment#

class datarobotx.Deployment(deployment_id=None)#

DataRobot ML Ops deployment.

Implements real-time predictions on ML Ops deployments

Parameters:: deployment_id (str, optional) – DataRobot id for the deployment from which to initialize the object

Attributes:

DataRobot python client datarobot.Deployment object.

Methods:

`from_url`(url)	Class method to initialize from a URL string.
`predict`(X[, batch_mode, max_explanations, ...])	Make predictions on X asynchronously using the deployment.
`predict_proba`(X[, batch_mode, ...])	Calculate class probabilities on X asynchronously using the deployment.
`predict_unstructured`(X)	Make predictions with data asynchronously using the deployment.
`share`(emails)	Share a deployment with other users.

property dr_deployment: Deployment#

DataRobot python client datarobot.Deployment object.

classmethod from_url(url)#

Class method to initialize from a URL string.

Useful for copy and pasting between GUI and notebook environments

Parameters:: url (str) – URL of a DataRobot GUI page related to the deployment of interest
Returns:: model – The deployed model object
Return type:: Deployment

predict(X, batch_mode=False, max_explanations=None, as_of=None, for_dates=None, **kwargs)#

Make predictions on X asynchronously using the deployment.

Returns empty DataFrame which will be updated with results when complete

Parameters:

X (pd.DataFrame or str) – Data to make predictions on. For text generation deployments, the prompt can be passed as a single string in which event it is converted for use in LLM playground models where the column will be named promptText
batch_mode (bool (default=False)) – If True, use batch mode for predictions
max_explanations (int or 'all' (default=None)) – Number of explanations to return for each prediction. Note that ‘all’ is supported for deployments using SHAP models only.
as_of (str (default=None)) – Applies to time series only. Forecast point to use for predictions. If not provided on a forecast, the latest forecast point will be used. Note that dates passed in are parsed using pd.to_datetime.
for_dates (str or tuple of str (default=None)) – Applies to time series only. Date(s) to return predictions for. If a single date is specified, a single prediction will be returned for that date. If a tuple of dates is specified, a prediction will be returned for each date in the range. Note that dates passed in are parsed using pd.to_datetime.
**kwargs (Any) – Additional optional predict-time parameters to pass to DataRobot Examples: ‘forecast_point’, ‘predictions_start_date’, ‘predictions_end_date’, ‘relax_known_in_advance_features_check’

Return type:

DataFrame

predict_proba(X, batch_mode=False, max_explanations=None, as_of=None, for_dates=None, **kwargs)#

Calculate class probabilities on X asynchronously using the deployment.

Returns empty DataFrame which will be updated with results when complete

Parameters:

X (pd.DataFrame) – Data to make predictions on
batch_mode (bool (default=False)) – If True, use batch mode for predictions
max_explanations (int or 'all' (default=None)) – Number of explanations to return for each prediction. Note that ‘all’ is supported for deployments using SHAP models only.
as_of (str (default=None)) – Applies to time series only. Forecast point to use for predictions. If not provided on a forecast, the latest forecast point will be used. Note that dates passed in are parsed using pd.to_datetime.
for_dates (str or tuple of str (default=None)) – Applies to time series only. Date(s) to return predictions for. If a single date is specified, a single prediction will be returned for that date. If a tuple of dates is specified, a prediction will be returned for each date in the range. Note that dates passed in are parsed using pd.to_datetime.
**kwargs (Any) – Additional optional predict-time parameters to pass to DataRobot Examples: ‘forecast_point’, ‘predictions_start_date’, ‘predictions_end_date’, ‘relax_known_in_advance_features_check’

Return type:

DataFrame

predict_unstructured(X)#

Make predictions with data asynchronously using the deployment.

Returns empty dict which will be updated with results when complete

share(emails)#

Share a deployment with other users. Sets the user role as an owner of the deployment.

Parameters:: emails (Union[str, list]) – A list of email addresses of users to share with