Hi, in this lesson,
you will learn how to monitor data drift. With Azure machine learning, you typically train a machine learning
model using a historical data set that is representative of the new data that
your model will receive for influencing. However over time there may be trends
that change the profile of the data, making your model less accurate. For example, suppose a model is trained
to predict the expected gas mileage of an automobile based on the number of
cylinders, engine size, weight and other features. Over time, as car manufacturing and engine
technologies advance the typical fuel efficiency of vehicles
might improve dramatically, making the predictions made by the model
trained on older data less accurate. This change in data profiles between
training and influencing is known as data drift and it can be a significant issue
for predictive models used in production. It is therefore important to be able
to monitor data drift over time and retrain models as required to
maintain predictive accuracy. In this lesson, you will learn how
to create a data drift monitor, scheduled data drift monitoring, and
view data drift monitoring results.