Устойчивость ML-моделей Как часто дообучать модель и как делать это правильно. О том, что модели нужно не только делать, но и поддерживать + пара советов на этом пути. Слайды тут https://kolodezev.ru/ekaterinburg2023.html

A primer on the math, logic, and pragmatic application of JS Divergence — including how it is best used in drift monitoring

Deploying machine learning models has become a relatively frictionless process. However, properly deploying a model with a robust testing and monitoring framework is a vastly more complex task. There is no one-size-fits-all solution when it comes to productionizing…

Over time, our AI predictions degrade. Full Stop.

Whether it’s concept drift, where the relationships of our data to what we’re trying to predict has changed, or data drift, where our production data no longer resembles the historical training data, identifying…