Can We Solve Distribution Shift With Clever Training In Machine Learning?
One of the biggest problems we have when using machine learning in practice is distribution shift. A distribution shift occurs when the distribution of the data the model sees in production starts to look different than the data used to train it. A simple example that broke a lot of models was COVID. The quarantine simply changed how people behaved and the historical data became less representative. Another good example is credit card fraud....