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What is Evidently AI?

Evidently AI is an open-source Python library designed for data scientists and ML engineers to evaluate, test, and monitor data and ML models from validation to production. It provides a comprehensive view of data and ML model quality, allowing users to explore and debug potential issues. Evidently AI works by taking snapshots of data and model performance at different stages of the ML lifecycle and comparing them to identify changes in data quality and model performance. It features a modular approach with Reports, Test Suites, and a Monitoring Dashboard, and offers in-built metrics, tests, and visualizations to evaluate and test data and ML model quality.

Evidently AI Features

Comprehensive View

Evidently AI provides a comprehensive view of data and ML model quality to explore and debug potential issues.
in-Built Metrics and Visualizations

Evidently AI offers a library of in-built metrics, tests, and visualizations to evaluate and test data and ML model quality throughout the model lifecycle.
Modular Approach

Evidently AI follows a modular approach with Reports, Test Suites, and a Monitoring Dashboard, allowing users to choose components as per their specific needs.
Self-Hostable Dashboard

Evidently AI enables users to self-host an ML monitoring dashboard to track model performance over time.

Evidently AI Use Cases

Continuous Monitoring

Evidently AI can be used for continuous monitoring of ML models throughout the entire lifecycle, identifying model drift and data quality issues to proactively address potential problems.
Model Validation

Evidently AI enables model validation by running data quality reports during exploratory data analysis (EDA), highlighting unstable features or areas that require further engineering.
Model Debugging

With Evidently AI, users can effectively debug their ML models by identifying changes in data quality and model performance, providing a comprehensive view of data and model quality for thorough exploration and debugging.

Related Tasks

Data Validation

Evidently AI helps in validating the quality and integrity of data used for training ML models.
Model Performance Monitoring

Evidently AI allows for continuous monitoring of ML model performance to detect any deviations or degradation.
Feature Importance Analysis

With Evidently AI, users can analyze the impact and importance of individual features on model performance.
Data Drift Detection

Evidently AI can identify changes in data distribution over time, helping to detect data drift and its impact on model performance.
Metric Tracking and Visualization

Evidently AI provides built-in metrics and visualizations to track and visualize key performance indicators of ML models.
Statistical Analysis

Evidently AI enables users to perform statistical analysis on data and model outputs to gain insights and detect anomalies.
Model Comparison

Evidently AI facilitates comparing different versions of ML models to identify improvements or regressions in performance.
Reporting and Documentation

Evidently AI generates comprehensive reports and documentation that summarize findings and facilitate collaboration among team members.

Data Scientist

Data scientists can use Evidently AI to evaluate and monitor the quality of data and ML models during the development and deployment phases.
Machine Learning Engineer

ML engineers can utilize Evidently AI to test and validate ML models, perform data quality analysis, and track model performance over time.
Data Analyst

Data analysts can leverage Evidently AI to examine and assess data quality, identify potential issues, and evaluate the performance of ML models.
Data Engineer

Evidently AI enables data engineers to monitor and validate the quality of data pipelines and ensure that the input data meets the desired standards for ML models.
AI Researcher

AI researchers can use Evidently AI to gain insights into the performance and behavior of AI models and validate their research findings.
AI Consultant

AI consultants can utilize Evidently AI to provide quality assurance and ensure the reliability of ML models used by their clients.
Model Validator

Model validators can employ Evidently AI to conduct thorough analysis and validation of ML models, identify anomalies, and recommend improvements.
ML Operations Mlops Engineer

MLOps engineers can integrate Evidently AI into their ML monitoring pipelines to continuously track and evaluate the performance and quality of deployed ML models.

Evidently AI FAQs

What is Evidently AI?

Evidently AI is an open-source Python library for evaluating, testing, and monitoring data and ML models.

What are the key features of Evidently AI?

The key features include a comprehensive view of data and model quality, a consistent API, self-hostable dashboard, and in-built metrics and visualizations.

How does Evidently AI work?

Evidently AI works by taking snapshots of data and model performance at different stages, comparing them to identify changes in quality and performance.