online-learning

What problem does it solve?

This Skill helps you adapt models to streaming data, detect concept drift, and incrementally update models without full pipeline re-runs.

Core Features & Use Cases

• Drift Detection: Detects covariate, label, and concept drift using PSI, KS test, ADWIN, DDM, and Page-Hinkley methods.
• Incremental Learning: Supports partial_fit, warm-start, and true online learning with the river library.
• Train/Score Separation: Implements the model-as-data control stream pattern for hot-swap without restart.
• Event Time vs Processing Time: Handles event-time windowing and watermarks for accurate feature computation.
• Feature Stores: Briefly frames the use of feature stores like Feast and Tecton for consistency and backfills.

Quick Start

Use the online-learning skill to detect drift in your model's input features and update the model incrementally.