![]() Welcome to Evidently, a tool that helps evaluate ML models during validation, and monitor them in production. Our advice: if you are going to rely on ML continuously in time, you need to have a ML monitoring system in place that will alert you when stuff goes wrong. Maybe the data is right, but the predictions start slowly misbehaving, leading you to make all sorts of bad business decisions in the process (losing lots of money!). Maybe the kind of data that the model is receiving in production is different than what it was used for training, causing it to underperform. In reality, there are a lot of things that can go wrong, often in unexpected ways. ![]() The ML people move to tackle some other, important problem. It starts receiving data and sending its predictions to populate very important dashboards. After teams of data scientists and ML engineers have done their work for the past few months, a ML model gets into production.
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