The machine learning system is now up and running. To make sure everything is on the right track, we need to conduct a performance check on a regular basis. To do so, besides making a prediction in real time, we should record the ground truth at the same time.

Continue the diabetes example with a performance check:

>>> from sklearn.metrics import r2_score 
>>> print('Health check on the model, R^2: 
    {0:.3f}'.format(r2_score(y_new, predictions)))
Health check on the model, R^2: 0.613

We should log the performance and set an alert for a decayed performance.