evidently open source analysis

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Project overview

⭐ 6966 · Jupyter Notebook · Last activity on GitHub: 2026-01-05

GitHub: https://github.com/evidentlyai/evidently

Why it matters for engineering teams

Evidently addresses the practical challenge of monitoring and validating machine learning models and data pipelines in production environments. It provides a comprehensive set of over 100 metrics to detect data drift, assess data quality, and ensure model performance remains consistent over time. This open source tool for engineering teams is particularly suited to machine learning and AI engineering roles that require ongoing observability of both tabular data and generative AI systems. Evidently is mature and reliable enough for production use, offering detailed reports and integration with Jupyter Notebooks for ease of analysis. However, it may not be the best choice for teams seeking a fully managed cloud service or those working exclusively with non-tabular data formats, where specialised solutions might offer tighter integration or additional automation.

When to use this project

Evidently is a strong choice when teams need a self hosted option for continuous monitoring of ML models and data pipelines, especially in environments where transparency and control are priorities. Consider alternatives if your focus is on real-time streaming data monitoring or if you require a fully managed SaaS platform with minimal setup.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from Evidently, using it to evaluate model drift, validate data quality, and generate detailed HTML reports for stakeholders. It is commonly employed in production ready solutions involving predictive analytics, fraud detection, and generative AI applications, where maintaining model accuracy and data integrity is critical.

Best suited for

Topics and ecosystem

data-drift data-quality data-science data-validation generative-ai hacktoberfest html-report jupyter-notebook llm llmops machine-learning mlops model-monitoring pandas-dataframe

Activity and freshness

Latest commit on GitHub: 2026-01-05. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.