NYorkTaxiFairPrediction.py

 def mlflow_run(self, name='n-york-taxi-test-run'):
        """
        :param name: Name of the run to be logged by MLflow
        :return: Tuple (ExperimentID, runID)
        """

        with mlflow.start_run(run_name=name) as run:
            # retrieve current run and experiment id
            runID = run.info.run_uuid
            experimentID = run.info.experiment_id
            
            # preprocess data, create model and train/evaluate
            # with training and validation data
            self.load_scale_and_preprocess_data()
            self.create_model()
            self.train_and_evaluate()
            
            _params = self.params[0]
            metrics = self._metrics[0]

            mae = metrics['mean_absolute_error']
            mse = metrics['loss']

            # compute  regression evaluation metrics
            rmse = np.sqrt(mse)
            
            # ***************************** MLflow Tracking Start
            # Log input parameters
            mlflow.log_params(_params)
            # Log metrics
            mlflow.log_metric("mae", mae)
            mlflow.log_metric("mse", mse)
            mlflow.log_metric("rmse", rmse)
            # Log model
            mlflow.keras.log_model(self.model, "Keras_model for NY-Taxi dataset")
            # ***************************** MLflow Tracking End

            print("MLflow Run with run_id {} and experiment_id {}".format(runID, experimentID))
            print('Mean Absolute Error    :', mae)
            print('Mean Squared Error     :', mse)
            print('Root Mean Squared Error:', rmse)

            return (experimentID, runID)