Today, we’re extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear.
The first thing I learned when I started working with data is that there is no such thing as paying too much attention to data quality. Raise your hand if you’ve spent hours hunting down problems caused by unexpected NULL values or by exotic character encodings that somehow ended up in one of your databases.
As models are literally built from large amounts of data, it’s easy to see why ML practitioners spend so much time caring for their data sets. In particular, they make sure that data samples in the training set (used to train the model) and in the validation set (used to measure its accuracy) have the same statistical properties.