Monitored models

Monitored models are mlops app objects that all of the monitoring module is based around. They represent a trained ml model that is ready to be used to make predictions. MLOps library provides the user with functionality to create models, download ml model information from the app and to make predictions using Pandas DataFrames and model registered in the MLOps app

mlops.monitoring.create_model

Function creates monitored model within MLOps app

Arguments:

• model_name: string

  Unique name of the created name

• model_description: string, optional

  Description of monitored model

• iteration_dict: dictionary, optional

  Dictionary containing valid iteration data with a path to model. It is recommended to use the result of the iteration.end_iteration method

Returns:

• monitored_model: string

  Json data of monitored model

mlops.monitoring.get_model_by_name

Function for retrieving mlops monitored model from database

Arguments:

• model_name: string

  Unique name of the monitored model to be retrieved

Returns:

• monitored_model: dictionary

  Json data of monitored model

mlops.monitoring.set_active_model

Function for setting active model from monitored models

Arguments:

• model_name: string

  Name of monitored model, that will be set as active

Returns:

• result: string

  Information about new active model setup

mlops.monitoring.send_prediction

Function to invoke a prediction from monitored model. Function accepts a Pandas dataframe, where every record is taken as a separate prediction.

Arguments:

• model_name: string

  Name of monitored model that will be used in prediction

• data: Pandas Dataframe

  Pandas Dataframe containing data for prediction. Each row of data in the dataframe is used for a separate prediction

• send_email: bool, optional

  If true email will be sent to email address specified in library settings. False by default.

Returns:

• prediction: list of dictionaries

  List of dictionaries containing results for each executed prediction