use templates for common model types and add custom metrics for anything else.find the root cause of model quality drops with ready-made dashboards. get startedlearn more →llm and nlp modelskeep tabs on text-based models and unstructured data.monitor the quality of model responses and data inputs.extract meaningful descriptors from text data and track how they evolve.detect distribution drift in texts and embeddings to spot the change before you get the labels.get startedlearn more →join 2,000+ data scientists and ml engineersget support, contribute, and chat ml in production in our discord community.join discordwhat the community saysdayle fernandesmlops 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.”read the blog →moe antarsenior 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.”javier lópez peñadata 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.”read the blog →jonathan bownmlops 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 wilsonprincipal rsa, databricks“check out evidently: i haven't seen a more promising model drift detection framework released to open-source yet!”niklas von maltzahnhead 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!”manoj kumardata scientist, walmart labs“i was searching for an open-source tool, and evidently perfectly fit my requirement for model monitoring in production. it was very simple to implement, user-friendly and solved my problem!”emmanuel rajsenior machine learning engineer, tietoevry“i love the plug-and-play features for monitoring ml models.”how it worksturn predictions to metrics, and metrics to dashboards.1. pick your preset decide what to collect: from individual metrics to complete statistical data snapshots. customize everything or go with defaults.2. log snapshotscapture metrics, summaries, and test results with evidently python library. send data from anywhere in your pipeline, batch or real-time.3. get a dashboardvisualize the results on a monitoring dashboard. explore your data over time, customize the views, and share with others on your team.