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Used in 1000s companies, from startups to enterprise.

Our platform is built on top of Evidently, a trusted open-source ML monitoring tool.
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Dayle Fernandes
Dayle Fernandes
MLOps Engineer, DeepL
“It is like a Swiss army knife we use more often than expected.”
Moe Antar
Moe Antar
Senior Data Engineer, PlushCare
“It has become an invaluable tool, enabling us to flag model drift and data quality issues directly from our CI/CD and model monitoring DAGs.”
Maltzahn
Niklas von Maltzahn
Head of Decision Science, JUMO
“Evidently is a first-of-its-kind monitoring tool that makes debugging machine learning models simple and interactive.”
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All testimonials

Dayle Fernandes
Dayle Fernandes
MLOps Engineer, DeepL
“We use Evidently daily to test data quality and monitor production data drift. It takes away a lot of headache of building monitoring suites, so we can focus on how to react to monitoring results. Evidently is a very well-built and polished tool. It is like a Swiss army knife we use more often than expected.”
Moe Antar
Moe Antar
Senior Data Engineer, PlushCare
“We use Evidently to continuously monitor our business-critical ML models at all stages of the ML lifecycle. It has become an invaluable tool, enabling us to flag model drift and data quality issues directly from our CI/CD and model monitoring DAGs. We can proactively address potential issues before they impact our end users.”
Jonathan Bown
Jonathan Bown
MLOps Engineer, Western Governors University
“The user experience of our MLOps platform has been greatly enhanced by integrating Evidently alongside MLflow. Evidently's preset tests and metrics expedited the provisioning of our infrastructure with the tools for monitoring models in production. Evidently enhanced the flexibility of our platform for data scientists to further customize tests, metrics, and reports to meet their unique requirements.”
Ben Wilson
Ben Wilson
Principal RSA, Databricks
“Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!”
Emmanuel Raj
Emmanuel Raj
Senior ML Engineer, TietoEVRY
“I love the plug-and-play features for monitoring ML models.”
Valentin Min
Ming-Ju Valentine Lin
ML Infrastructure Engineer, Plaid
“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.”
Evan Lutins
Evan Lutins
Machine Learning Engineer, Realtor.com
“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.”
Javier Lopez Peña
Javier López Peña
Data Science Manager, Wayflyer
“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.”
Maltzahn
Niklas von Maltzahn
Head of Decision Science, JUMO
“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!”

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