Used in 1000s companies, from startups to enterprise.

“At Wise, Evidently proved to be a great solution for monitoring data distribution in our production environment and linking model performance metrics directly to training data. Its wide range of functionality, user-friendly visualization, and detailed documentation make Evidently a flexible and effective tool for our work. These features allow us to maintain robust model performance and make informed decisions about our machine learning systems.”

“We use Evidently for continuous model monitoring, comparing daily inference logs to corresponding days from the previous week and against initial training data. This practice prevents score drifts across minor versions and ensures our models remain fresh and relevant. Evidently’s comprehensive suite of tests has proven invaluable, greatly improving our model reliability and operational efficiency.”

“At Realtor.com, we implemented a production-level feature drift pipeline with Evidently. This allows us detect anomalies, missing values, newly introduced categorical values, or other oddities in upstream data sources that we do not want to be fed into our models. Evidently's intuitive interface and thorough documentation allowed us to iterate and roll out a drift pipeline rather quickly.”

“Evidently is a fantastic tool! We find it incredibly useful to run the data quality reports during EDA and identify features that might be unstable or require further engineering. The Evidently reports are a substantial component of our Model Cards as well. We are now expanding to production monitoring.”

“Evidently is a first-of-its-kind monitoring tool that makes debugging machine learning models simple and interactive. It's really easy to get started!”