Three Common Ways of Online Machine Learning

THREE PRODUCTION PATTERNS

Name exactly what is online.

All three may coexist, but they carry different state, latency, failure modes and evaluation protocols.

Online Inference

The model is temporarily fixed; predictions are immediate.

The most common production meaning—even though no learning occurs per request.

Request

→

Online features

→

Model server

→

Prediction + log

What must be fast

Feature lookup, preprocessing parity, model execution and response serialization.

Typical stack

Feast/Redis · KServe/Triton/BentoML · XGBoost/ONNX/PyTorch.

Examples

Fraud, product ranking, credit scoring and image inference.

request → online_features(entity_id, event_time) → model.predict(feature_vector) → log(prediction_id, feature_version, model_version)

Online Feature Computation

Features evolve continuously; training may remain batch-oriented.

This powers much fraud and recommendation. Freshness often comes from features, not new weights.

Kafka / events

→

Flink / SQL / IVM

→

Feature state

→

Inference

Maintained state

Count, sum, mean, variance, distinct entities, recency, histogram and co-occurrence.

Time semantics

Event time, processing time, late events, watermarks, sliding windows and expiration.

Examples

5-minute velocity, 7-day spend and 24-hour CTR.

new_event(entity=104, amount=2500) → count_5m += 1 → sum_7d += 2500 → expire old contributions → serve revised features

True Online Learning

Predict first; update model state when its label arrives.

Use when the environment changes rapidly, feedback is trustworthy and update risk is controlled.

xₜ

→

predict_one

→

delayed yₜ

→

learn_one + checkpoint

Suitable models

Online linear/logistic models, Naive Bayes, Hoeffding trees, bandits and incremental anomaly detection.

Typical tools

River · Vowpal Wabbit · sufficient-statistic updates.

Risks

Poisoning, feedback loops, catastrophic forgetting, label delay and rollback.

ŷₜ = predict_one(xₜ) → log evidence → wait for verified yₜ → metric.update(yₜ, ŷₜ) → learn_one(xₜ, yₜ) → checkpoint

DECISION TABLE

Choose from latency and change.

Per-event model updates are not automatically more mature. Many systems should stop at Pattern 1 or 2.

Question	Online inference	Online features	True online learning
Immediate change	Prediction	Feature state	Model parameters
Immediate label?	No	No	No, but it must be joinable later
Best when	Response must be fast	Recent behaviour matters	Concept changes rapidly
Evidence	Latency, availability	Freshness, skew, drift	Progressive loss, regret, stability
Rollback	Model version	Feature state/version	Checkpoint + replay log

SCENARIOS

Different products need different kinds of online.

Select a case to inspect prediction latency, feature freshness and label delay.

Payment Fraud

Inference precedes authorization; velocity must be fresh, while chargeback truth may arrive days later.

<100 msinference

1–10 sfeatures

dayslabel delay

Recommendation

Popularity and session context move quickly; large embeddings refresh periodically and bandits handle exploration.

<50 msranking

secondssession

minutes+feedback

Predictive Maintenance

Sensor features stream continuously, but rare failure labels favour drift-gated micro-batch retraining.

secondswindows

rarelabels

scheduledretraining

DRIFT & DELAYED TRUTH

Prediction happens now; truth may arrive next week.

Preserve prediction identity, event time, feature version and model version so delayed truth reconnects to evidence.

Separate the signals

Feature drift: P(X) · Label drift: P(Y) · Concept drift: P(Y|X) · Prediction drift: P(Ŷ) · Performance drift: loss after labels arrive.

prediction_log(prediction_id, entity_id, event_time, feature_version, model_version, predicted_label, probability)
verified_labels(prediction_id, label_time, actual_label)
evaluate by prediction event window—not only label arrival time

PRACTICAL WARIN.ME STACK

Small enough to learn; real enough to expose production boundaries.

Use ClickHouse as the evidence/state engine, adding River or Vowpal Wabbit only for true online updates.

Events

→

ClickHouse state

→

Inference API

→

Drift + labels

→

River/VW or retrain

Feast ↗KServe ↗Vowpal Wabbit ↗River ↗