pjt222/run-ab-test-models

A/B-Tests fuer Modelle durchfuehren

See Extended Examples for complete configuration files and templates.

Ausfuehren controlled experiments comparing model versions using traffic splitting and statistical analysis.

Wann verwenden

• Deploying new model version and want to validate improvement vor full rollout
• Comparing multiple candidate models trained with different algorithms or features
• Testing impact of hyperparameter changes on business metrics
• Need to measure model performance in production ohne risking full traffic
• Regulatory requirements for gradual rollout (e.g., medical ML systems)
• Evaluating cost-performance tradeoffs zwischen model sizes

Eingaben

• Erforderlich: Champion model (current production version)
• Erforderlich: Challenger model(s) (new version to test)
• Erforderlich: Traffic allocation percentage (e.g., 5% to challenger)
• Erforderlich: Success metrics (business and ML metrics)
• Erforderlich: Minimum sample size or test duration
• Optional: Guardrail metrics (latency, error rate thresholds)
• Optional: User segments for stratified testing

Vorgehensweise

Schritt 1: Entwerfen Experiment

Definieren test parameters, success criteria, and statistical requirements.

# ab_test/experiment_config.py
from dataclasses import dataclass
from typing import List, Dict
import numpy as np
from scipy.stats import norm


@dataclass
# ... (see EXAMPLES.md for complete implementation)

Erwartet: Experiment configuration with statistically sound sample size calculation, typischerweise 5-10k samples per variant for 5-10% MDE.

Bei Fehler: If required sample size too large, increase traffic allocation, extend test duration, or accept larger MDE; verify baseline metric estimate is accurate; consider sequential testing for continuous monitoring.

Schritt 2: Implementieren Traffic Splitting

Einrichten routing logic to randomly assign requests to models.

# ab_test/traffic_router.py
import hashlib
import random
from typing import Dict, Optional
from dataclasses import dataclass
import logging

logger = logging.getLogger(__name__)
# ... (see EXAMPLES.md for complete implementation)

Erwartet: Consistent user-to-variant assignment, accurate traffic split matching configured percentages, all assignments logged for analysis.

Bei Fehler: Verifizieren hash function produces uniform distribution (test with 10k user IDs), check that user_id is stable across requests (not session_id), ensure logs capture all prediction events, validate traffic split in first 1000 requests.

Schritt 3: Implementieren Shadow Deployment (Optional)

Ausfuehren challenger model in parallel ohne affecting users (shadow mode).

# ab_test/shadow_deployment.py
import asyncio
from typing import Dict, Any
import logging
from concurrent.futures import ThreadPoolExecutor
import time

logger = logging.getLogger(__name__)
# ... (see EXAMPLES.md for complete implementation)

Erwartet: Champion predictions served with normal latency, challenger predictions logged asynchronously ohne blocking, prediction differences captured for analysis.

Bei Fehler: Set challenger timeout < champion SLA to avoid blocking, handle challenger errors gracefully ohne affecting champion, monitor memory usage (two models loaded), consider sampling (log only 10% of shadow predictions).

Schritt 4: Sammeln and Analysieren Metrics

Sammeln experiment data and perform statistical tests.

# ab_test/analysis.py
import pandas as pd
import numpy as np
from scipy import stats
from typing import Dict, Tuple
import logging

logger = logging.getLogger(__name__)
# ... (see EXAMPLES.md for complete implementation)

Erwartet: Statistical test results with p-values, confidence intervals, and clear decision (rollout/keep/inconclusive), typischerweise nach 7-14 days or reaching sample size.

Bei Fehler: Verifizieren ground truth labels are available (may need delayed analysis), check for sample ratio mismatch (SRM) indicating assignment bugs, ensure sufficient sample size reached, look for novelty/primacy effects in early data, consider sequential testing if fixed-horizon test is too slow.

Schritt 5: Ueberwachen Guardrail Metrics

Continuously check that challenger doesn't violate safety thresholds.

# ab_test/guardrails.py
import pandas as pd
import logging
from typing import Dict, List

logger = logging.getLogger(__name__)


# ... (see EXAMPLES.md for complete implementation)

Erwartet: Guardrail violations detected innerhalb 5-15 minutes, automated experiment stop if critical thresholds breached (latency, errors), alerts sent to team.

Bei Fehler: Verifizieren guardrail thresholds are realistic (not too tight), ensure monitoring loop is running continuously, check that stop_experiment() function actually updates routing, test alert delivery channels.

Schritt 6: Make Rollout Decision

Based on experiment results, decide whether to rollout challenger.

# ab_test/rollout_decision.py
import logging
from typing import Dict
from dataclasses import dataclass

logger = logging.getLogger(__name__)


# ... (see EXAMPLES.md for complete implementation)

Erwartet: Clear decision (full/gradual rollout, keep champion, or extend test) with justification and action items.

Bei Fehler: If decision unclear, perform subgroup analysis (by user segment, time of day, device type), check for interaction effects, review business context (e.g., is 2% lift worth engineering cost?), consult with stakeholders.

Validierung

• [ ] Traffic split matches configured percentages (innerhalb 1%)
• [ ] Same user always assigned to same variant (consistency check)
• [ ] Sample size calculation produces reasonable numbers (5-50k per variant)
• [ ] Statistical tests produce p-values consistent with manual calculation
• [ ] Guardrail violations trigger alerts innerhalb 5 minutes
• [ ] Shadow deployment shows <5% prediction divergence zwischen models
• [ ] Experiment reports include confidence intervals
• [ ] Rollout decision documented with justification

Haeufige Stolperfallen

• Sample ratio mismatch (SRM): If observed traffic split differs from configured (e.g., 95/5 becomes 92/8), indicates assignment bug; check hash function uniformity
• Peeking: Checking results vor reaching sample size inflates Type I error; use sequential testing or wait for pre-determined end date
• Novelty effect: Users respond differently to new model initially; run for 2+ weeks to see steady-state behavior
• Carryover effects: Previous variant exposure affects current behavior; use new users or sufficient washout period
• Multiple testing: Testing many metrics increases false positive risk; correct with Bonferroni or focus on single primary metric
• Insufficient power: Small traffic allocation may require months to detect realistic effects; balance statistical power with risk tolerance
• Ignoring segments: Aggregate lift may hide negative impact on important user segments; perform subgroup analysis
• Attribution errors: Sicherstellen outcome metrics korrekt attributed to model predictions (not other system changes)

Verwandte Skills

• deploy-ml-model-serving - Modellieren deployment infrastructure and versioning
• monitor-model-drift - Ongoing performance monitoring post-rollout