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Evidently

Open-source ML observability, model evaluation, and data drift monitoring library.

LicenseApache-2.0
GitHub stars5.5k
Last commit1 weeks ago
Tags5 topics
Llm EvaluationModel MonitoringPythonData Drift DetectionMl Observability
Overview

Why consider Evidently?

Evidently is an open-source evaluation and monitoring framework for machine learning models and LLMs written in Python. It detects data distribution drift, concept drift, missing value spikes, and prediction degradation, generating interactive visual HTML test reports and telemetry metrics.

Guided learning

Learn Evidently by building

Practical setup notes, real use cases, and copy-ready examples in one focused guide.

1 min read 2 sections
In this guide2 sections

Overview of Evidently

Evidently tracks data distributions and alerts on covariate shift before model accuracy degrades.

Quickstart

bash
1pip install evidently
2evidently ui

Evidently is licensed under the Apache License Version 2.0.

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Evidently FAQs

Evidently is listed as a Developer Tools tool on TiloBox. Review the overview, features, and official documentation on this page to decide whether it solves your specific workflow.

Start with the project's GitHub repository and official website for supported installation and deployment instructions. Test the setup with representative data or a small project before rolling it out more widely.

Evidently is listed under the Apache-2.0 license. Read the complete license text and the project's notices before using, modifying, or distributing the software.

Production readiness depends on your requirements. Review maintenance activity, security practices, documentation, backup and upgrade procedures, and compatibility with your stack; then validate it in a non-production environment.

Evidently is listed as an alternative to WhyLabs. Compare the core workflow, deployment model, integrations, and licensing against your must-have requirements before switching.