Evidently AI

What is Evidently AI?

Evidently AI is a comprehensive solution designed to help teams build, evaluate, and maintain reliable AI products, including traditional machine learning models and large language model (LLM) applications. It offers an open-source Python library with over 100 built-in evaluation metrics and a cloud platform that supports AI testing, monitoring, synthetic data generation, and collaborative workflows. Evidently AI enables users to detect data and prediction drift, perform regression and adversarial testing, and manage datasets and evaluations through an intuitive interface, ensuring continuous AI quality across the product lifecycle.

Key Features

• Extensive Evaluation Metrics

  Provides 100+ built-in metrics covering data quality, model performance, drift detection, and LLM-specific evaluations for comprehensive AI assessment.

• Open-Source Python Library

  A modular, developer-friendly library with a declarative API for running evaluations locally, enabling flexible integration and customization.

• Evidently Cloud Platform

  A no-code interface for managing projects, datasets, evaluations, and dashboards, supporting collaboration and real-time monitoring with alerting.

• Synthetic Data and Adversarial Testing

  Tools to generate synthetic datasets and design adversarial test scenarios to stress-test AI models for robustness and safety.

• Drift and Performance Monitoring

  Continuous tracking of data drift, target drift, and prediction drift with alerting mechanisms to maintain model accuracy in production.

• Support for ML and LLM Workflows

  Unified support for classical machine learning and large language model applications, enabling evaluation across diverse AI use cases.

Use Cases

• Model Performance Validation : Evaluate and monitor model accuracy, precision, recall, and other metrics to ensure AI systems perform as expected.
• Data Drift Detection : Identify shifts in input data or target distributions that could degrade model quality over time, enabling proactive interventions.
• AI System Monitoring : Track AI outputs in production environments with dashboards and alerts to detect anomalies and maintain reliability.
• Collaborative AI Quality Management : Facilitate teamwork by sharing evaluation results, dashboards, and test cases across data scientists, engineers, and domain experts.
• Synthetic and Adversarial Testing : Create synthetic datasets and adversarial inputs to test AI system robustness and safety under edge cases.

FAQs