Introduction
Evidently is an open-source toolset for monitoring data quality, detecting drift, tracking model performance, and visualizing ML health metrics in live environments. Evidently bridges the gap between model development and production monitoring by helping teams understand how model inputs, predictions, and performance metrics evolve in real-world conditions. Evidently is integrated into model pipelines to surface early warnings for data drift, distribution shifts, and degraded inference behavior, enabling faster iteration and more robust deployments.
Key benefits of using Evidently within the Cake platform include:
Data and Prediction Drift Detection: Compares distributions of features and outputs across time or environments to detect significant shifts that may indicate upstream changes or model decay.
Customizable Monitoring Reports: Automatically generates rich, shareable reports with statistical tests, visualizations, and alerts for datasets, training pipelines, and live inference streams.Performance Monitoring and Fairness Audits: Tracks classification or regression performance (e.g., accuracy, precision, RMSE) and evaluates fairness across defined segments or demographic groups.
Integration with CI/CD and Pipelines: Easily incorporated into batch pipelines (e.g., Airflow, PipeCat), notebooks, or real-time scoring flows for continuous validation and monitoring.Lightweight and Framework-Agnostic: Works with common formats like Pandas DataFrames, Parquet files, and SQL queries, making it easy to integrate with ML workflows across frameworks.
Evidently is used across model validation, deployment, and post-deployment monitoring—covering both traditional ML models and embeddings-based systems. It is particularly valuable for tracking feature drift in production, evaluating the consistency of RAG pipelines, and establishing baselines before model updates or retraining. By adopting Evidently, you can ensure that its machine learning systems are monitored, explainable, and resilient over time—empowering teams to detect issues early and continuously deliver high-quality, data-driven experiences.