Introduction

When you train a model on time series data, is common to only use a recent window of time for your training data. This is done because of the reasonable assumption that more recent data is probably more representative of what's going to happen in the immediate future. And just like a new car losing a large percentage its value the moment it's driven off the car lot, our production models will become less and less predictive overtime due to a process called drift. To day on the show, i speak with sam ackerman about how to detect drift in outliers that can affect our machine learning models too.