Evidently AI
Evidently AI is an open-source Python library for monitoring ML models, offering data drift, quality, and performance metrics, suitable for data scientists and ML engineers.
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What is Evidently AI?
Evidently AI is an open-source Python library that monitors machine learning models, supporting evaluation from Retrieval-Augmented Generation (RAGs) systems to AI assistants driven by large language models (LLMs). It provides tools for monitoring data drift, data quality, and production ML model performance. With over 20 million downloads and 5,000 GitHub stars, it's a trusted tool in the machine learning field.
Who Can Benefit from Evidently AI?
Data scientists, ML engineers, and AI product managers can benefit from Evidently AI because it offers a comprehensive toolkit for assessing AI products from development through to production. This helps them systematically check, test, and monitor AI products, ensuring model stability and data quality.
Example Usage Scenarios:
MLOps engineers at DeepL use Evidently AI daily to test data quality and monitor production data drift.
Senior data scientists and AI leaders at Wise use Evidently AI to monitor data distribution in production environments and link model performance metrics back to training data.
Senior data engineers at PlushCare use Evidently AI to continuously monitor critical ML models and flag issues related to model drift and data quality within CI/CD pipelines and model monitoring workflows.
Key Features:
LLM Observability: Evaluate LLM-driven products like RAGs and AI assistants.
ML Observability: Monitor data drift, data quality, and performance of production ML models.
Open Source: An open-source Python library with over 200,000 downloads.
Custom Dashboards: View clear performance insights of AI products before and after deployment, and share results easily with teams.
Continuous Testing: Assess generated outputs to ensure accuracy, safety, and quality.
In-depth Debugging: Understand individual errors and convert bad outcomes into test cases for continuous improvement.
Predictive ML: Evaluate input and output quality for predictive tasks including classification, regression, ranking, and recommendation.
Getting Started with Evidently AI:
1. Visit the Evidently AI website and sign up for an account or start a free trial.
2. Choose the assessment, testing, or monitoring features you need.
3. Use Evidently’s custom dashboards and continuous testing capabilities to conduct deep analysis and evaluation of your AI products.
4. Design custom AI quality systems using built-in 100+ metrics or add your own custom metrics.
5. Utilize Evidently’s in-depth debugging features to understand and improve individual errors.
6. Ensure model input and output stability using data drift and data quality monitoring features.
7. Track model performance to make sure it meets expectations during deployment, retraining, and updates.
