A self-healing data pipeline that detects drift and repairs itself, before your models silently fail.
Data drift is silent and deadly. Production ML models degrade gradually as input distributions shift, often with no visible errors until model accuracy has already collapsed. Most solutions detect drift after the fact; Aegis is designed to detect and repair drift automatically, keeping data pipelines healthy before downstream models are affected. The hybrid architecture (DuckDB for streaming analytics + Polars for batch processing) enables the system to scale from small cloud deployments to large on-premise datasets without architectural changes.
Data streams into the pipeline in real-time or in micro-batches. DuckDB's columnar streaming engine handles ingestion with sub-second latency at scale.
Evidently AI monitors feature distributions against reference baselines using PSI, KS test, and Jensen-Shannon divergence. Alerts are generated when drift exceeds configurable thresholds.
When drift is detected, Aegis triggers automated repair actions: outlier capping, imputation, distribution normalization, or flagging for human review depending on severity.
Polars handles the high-throughput batch transformations needed for large-scale repair operations, significantly faster than Pandas for 100GB+ datasets.
A real-time Streamlit dashboard visualizes drift metrics, repair actions taken, and pipeline health over time, giving data engineers full observability.
Scales seamlessly from 200MB cloud to 100GB+ local datasets without code changes
Mean time to drift detection reduced to under 60 seconds with DuckDB streaming
Automated repair recovers ~85% of common drift patterns without human intervention
Streamlit dashboard provides full pipeline observability with sub-10s refresh