Module online 

Online learning and dynamic retraining infrastructure Online Learning Infrastructure for Dynamic Model Retraining

This module provides incremental model updates without full retraining, supporting continuous improvement in production ML systems.

References

• [Bottou 2010] “Large-Scale Machine Learning with Stochastic Gradient Descent”
• [Crammer et al. 2006] “Online Passive-Aggressive Algorithms”

Toyota Way Principles

• Kaizen: Continuous model improvement via online learning
• Jidoka: Drift detection stops bad predictions automatically
• Just-in-Time: Retrain only when drift detected, not on schedule

Modules

corpus

Corpus Management for Online Learning

curriculum

Curriculum Learning for Progressive Training

distillation

Knowledge Distillation for Model Compression

drift

Drift Detection for Triggering Model Retraining

orchestrator

Retrain Orchestrator for Drift-Triggered Model Updates

Structs

OnlineLearnerConfig

Configuration for online learning

OnlineLinearRegression

Simple online linear regression using SGD

OnlineLogisticRegression

Simple online logistic regression using SGD

Enums

LearningRateDecay

Learning rate decay schedules

Traits

OnlineLearner

Online learning capability for incremental model updates

PassiveAggressive

Passive-Aggressive online learning for classification