Evidently AI

Evidently AI is an open-source ML monitoring and observability platform that helps data science and ML engineering teams evaluate, test, and monitor the performance of machine learning models in production. Founded in 2021 and headquartered in London, United Kingdom, Evidently has quickly gained recognition in the ML community for providing elegant, practical tools for detecting data drift, model degradation, and data quality issues, capabilities that are essential for responsible AI governance in production environments. Evidently's core offering is built around three product components: Evidently Open Source, a Python library for ML model evaluation and testing that generates interactive visual reports and dashboards; Evidently Cloud, a managed platform for continuous ML monitoring with dashboards, alerting, and team collaboration; and Evidently Enterprise, offering additional features for larger organizations including role-based access control, SSO, and enhanced data retention. The open-source library can be used standalone for one-off model evaluations or integrated into production pipelines for continuous monitoring. The platform excels at detecting various types of drift and data quality issues: data drift (changes in input feature distributions), prediction drift (changes in model output distributions), concept drift (changes in the relationship between inputs and targets), and target drift (changes in ground truth distributions). Evidently provides over 100 pre-built metrics and test suites covering model performance, data quality, data drift, and regression/classification-specific analyses. Reports are generated as interactive HTML dashboards that can be easily shared with stakeholders, making model monitoring results accessible to both technical and non-technical audiences. From a governance perspective, Evidently addresses critical monitoring and oversight requirements that are foundational to responsible AI practice. Continuous model monitoring helps organizations detect when models begin behaving unexpectedly, enabling proactive intervention before issues impact users. The platform's test suites can be integrated into CI/CD pipelines to create automated quality gates for model deployment, and monitoring dashboards provide ongoing visibility into model behavior for governance reporting. Evidently's excellent documentation and tutorials have contributed to its rapid community adoption. However, Evidently focuses specifically on monitoring and evaluation rather than providing a complete AI governance solution. Organizations will need to combine Evidently with other tools for model registry, deployment management, bias assessment, compliance tracking, and governance workflow management.