Adaptive model learning

1) Why adaptability

The world is changing faster than release cycles. Adaptive learning allows the model to adapt to new data/modes without completely re-building: maintain quality, reduce drift response time, and reduce cost of ownership.

• Stable quality when drifting source, feature, label, concept.
• Minimal latency between shear detection and parameter update.
• Controlled cost and risks (privacy/fairness/security).

2) Drift types and signals

Data (covariate) drift: X distribution has changed.
Label drift: class frequencies/labeling policy.

Signals: PSI/JS/KS by features, calibration monitoring, drop in metrics on holdout/proxysamers, increase in the share of overrides by humans, spikes in complaints/incidents.

3) Adaptation trigger

Threshold: PSI> X, p-value <α, calibration out of sync.
Temporary: daily/weekly/sliding windows.
Event: new product version, pricing, market entry.
Economic: cost-to-error/share of losses> limit.

Triggers are encoded as policy-as-code and reviewed.

4) Adaptive learning archetypes

1. Batch re-train: simple and reliable; reacts slowly.
2. Incremental/online learn: updating weights on the stream; instantly, but the risks of forgetting.
3. Warm-start fine-tune: initialization with the previous model, additional training in the fresh window.
4. PEFT/LoRA/Adapters (LLM/vectors): fast narrow updates without full FT.
5. Distillation/Teacher→Student: knowledge transfer when changing architecture/domain.

6. Domain adaptation/transfer: basis freezing + fine tuning of the "head."

7. Meta-learning/Hypernets: Speed up retraining with few examples.
8. Bandits/RL: policy adaptation in response to the response of the environment.
9. Federated learning: personalization without taking out raw data.

5) Data mode strategies

Streaming: online optimizers (SGD/Adam/Adagrad), EMA scales, sliding windows, rehearsal buffer for anti-forgetting.
Micro-batches: regular mini-fit (hour/day), early-stop by validation.
Batch windows: rolling 7/14/30d by domain, stratified for rare classes.
Few-shot: PEFT/Adapters, prompt-tuning, retrieval-inserts for LLM.

6) Catastrophic forgetting control

Rehearsal.
Regularization: EWC/LwF/ELR - penalty for moving away from previous importance.
Distillation: KLD to past model on anchor data.
Mixture-of-Experts/condition on context: Different specialists by segment.
Freeze- & -thaw: freezing of the basis, additional training of the upper layers.

7) Personalization and segmentation

Global + Local heads: common base, "heads" per segment (region/channel/VIP).
Per-user adapters/embeddings: easy memory for the user.
Gating by context: routing traffic to the best expert (MoE/routers).
Fairness Guards: Make sure personalization doesn't worsen group parity.

8) Active Learning (man-in-circuit)

Markup query strategies: maximum uncertainty, margin/entropy, core-set, violation committee.
Budgets and deadlines: daily markup quotas, response SLAs.
Markup acceptance: control of consent of annotators, small gold tests.
Loop closure: immediate additional training on new true labels.

9) Selection of optimizers and schedules

Online: Adagrad/AdamW with decay, clip-grad, EMA options.
Schedules: cosine restarts, one-cycle, warmup→decay.
For tabular: incremental GBDT (updating trees/adding trees).
For LLM: low lr, LoRA rank for the task, quality drop control according to the regulations.

10) Data for adaptation

Online buffer: fresh positive/negative cases, class balance.
Reweighting: importance weighting при covariate drift.
Hard-examples mining: heavy errors in priority.
Data contracts: schemes/quality/PII masks - the same as for the production stream.

11) Adaptive Quality Assessment

Pre-/Post-lift: A/B or interpreted quasi-experiment.
Rolling validation: time splits, out-of-time test.
Guardrails: calibration, toxicity/abuse, safe confidence thresholds.
Worst-segment tracking: Monitoring the worst segment, not just the average.
Staleness KPI: time since last successful adaptation.

12) MLOps: Process and Artifacts

Model Registry: version, date, data window, feature hash, hyper, artifacts (PEFT).
Data Lineage: from sources to feature store; freezing of training slices.
Pipelines: DAG для fit→eval→promote→canary→rollout, с auto-revert.
Shadow/Canary: comparison against the production version on real traffic.
Observability: latency/cost, drift, fairness, safety, override-rate.

Release policy: who and under what metrics clicks "promote."

13) Security, privacy, rights

PII minimization and masking, especially in streaming buffers.
Privacy-preserving adaptation: FL/secure aggregation, DP-clips/noises for sensitive domains.
Ethics: bans on autoadapt in high-risk solutions (human-in-the-loop is mandatory).
Alienation of knowledge: control of leaks through distillation/built-in trap keys.

14) Economics and SLO adaptations

SLA updates: for example, TTA (time-to-adapt) ≤ 4 hours when drifting.
Budget guardrails: GPU hours/day limits, cap on egress/storage.
Cost-aware policy: night windows, priority of critical models, PEFT instead of full FT.
Cache/retriever: for LLM - increase groundedness without full training.

15) Antipatterns

"Learn always and everywhere": uncontrolled online-fit → drift into the abyss.
Lack of rehearsal/regularization: catastrophic forgetting.

No offline/online eval: releases "by eye."

Retraining on complaints/appeals: exploitation of feedback by attackers.
Domain mixing: a single model for radically different segments without routing.
Zero traceability: you cannot reproduce what you have retrained on.

16) Implementation Roadmap

1. Discovery: drift map, segments, critical metrics and risks; Select the mode (batch/online/PEFT).
2. Monitoring: PSI/calibration/business guardrails; alerts and panels.
3. MVP adaptation: rolling window + warm-start; canary + auto-revert.
4. Safety/priv: masks, FL/DP if necessary; audit logs.
5. Active Learning: Markup loop with budget and SLA.
6. Scale: segmental heads/MoE, rehearsal buffers, distillation.
7. Optimization: PEFT/LoRA, cost-aware schedules, meta-learning, automatic trigger selection.

17) Checklist before enabling auto-adaptation

• Triggers (PSI/metrics), thresholds and windows, owner and escalation channel are defined.
• There is offline eval and online canary/shadow; guardrail-metrics and promote criteria.
• Rehearsal/distillation/regularization versus forgetting are included.
• Data/weights/PEFT deltas are versioned; window snapshot is stored.
• Privacy/PII policies imposed; Audit buffer access.
• Resource budgets and limits; emergency stop and auto-rollback.
• Documentation: Model Card (updated applicability zone), runbooks incidents.

18) Mini-templates (pseudo-YAML/code)

Policy AutoAdaptations

yaml adapt_policy:
triggers:
- type: psi_feature; feature: device_os; threshold: 0. 2; window: 7d
- type: metric_drop; metric: auc; delta: -0. 03; window: 3d mode: warm_start_finetune method:
lora: {rank: 8, alpha: 16, lr: 2e-4, epochs: 1}
rehearsal:
buffer_days: 30 size: 200k guardrails:
min_calibration: ece<=0. 03 worst_segment_auc>=0. 78 rollout: {canary: 10%, promote_after_hours: 6, rollback_on_guardrail_fail: true}
budgets: {gpu_hours_day: 40}

Online update (thumbnail)

python for batch in stream():
x,y = batch. features, batch. labels loss = model. loss(x,y) + reg_ewc(theta, theta_old, fisher, λ=0. 5)
loss. backward(); clip_grad_norm_(model. parameters(), 1. 0)
opt. step(); ema. update(model); opt. zero_grad()
if t % eval_k == 0: online_eval()

Active Learning Queue

yaml al_queue:
strategy: "entropy"
daily_budget: 3000 sla_labeling_h: 24 golden_checks: true

19) The bottom line

Adaptive training of models is not a "restart of training," but an engineering circuit: drift detection → safe and economical adaptation → quality and fairness testing → controlled release with the possibility of instant rollback. By combining monitoring, PEFT/online strategies, rehearsal against forgetting and strict guardrails, you get models that reliably change with the data and continue to deliver measurable benefits.