from seldon_core.metrics import SeldonMetrics # create a SeldonMetrics instance metrics = SeldonMetrics() # start the timer metrics.start_timer() # make a prediction with the model prediction = model.predict(data) # stop the timer and record the elapsed time metrics.stop_timer("prediction_time")
from seldon_core.metrics import SeldonMetrics # create a SeldonMetrics instance metrics = SeldonMetrics() # make predictions with the model for a set of test data predictions = model.predict(test_data) # record the true and false positives/negatives in the metrics object metrics.update_metrics(predictions, test_labels)In both cases, the SeldonMetrics package library is used to create an instance of the SeldonMetrics class, which can then be used to track various metrics related to a model's performance.