Run A/B Test for Models
See Extended Examples for complete configuration files and templates.
Execute controlled experiments comparing model versions using traffic splitting and statistical analysis.
When to Use
- Deploying new model version and want to validate improvement before 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 without risking full traffic
- Regulatory requirements for gradual rollout (e.g., medical ML systems)
- Evaluating cost-performance tradeoffs between model sizes
Inputs
- Required: Champion model (current production version)
- Required: Challenger model(s) (new version to test)
- Required: Traffic allocation percentage (e.g., 5% to challenger)
- Required: Success metrics (business and ML metrics)
- Required: Minimum sample size or test duration
- Optional: Guardrail metrics (latency, error rate thresholds)
- Optional: User segments for stratified testing
Procedure
Step 1: Design Experiment
Define 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)
Expected: Experiment configuration with statistically sound sample size calculation, typically 5-10k samples per variant for 5-10% MDE.
On failure: 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.
Step 2: Implement Traffic Splitting
Set up 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)
Expected: Consistent user-to-variant assignment, accurate traffic split matching configured percentages, all assignments logged for analysis.
On failure: Verify 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.
Step 3: Implement Shadow Deployment (Optional)
Run challenger model in parallel without 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)
Expected: Champion predictions served with normal latency, challenger predictions logged asynchronously without blocking, prediction differences captured for analysis.
On failure: Set challenger timeout < champion SLA to avoid blocking, handle challenger errors gracefully without affecting champion, monitor memory usage (two models loaded), consider sampling (log only 10% of shadow predictions).
Step 4: Collect and Analyze Metrics
Gather 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)
Expected: Statistical test results with p-values, confidence intervals, and clear decision (rollout/keep/inconclusive), typically after 7-14 days or reaching sample size.
On failure: Verify 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.
Step 5: Monitor 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)
Expected: Guardrail violations detected within 5-15 minutes, automated experiment stop if critical thresholds breached (latency, errors), alerts sent to team.
On failure: Verify 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.
Step 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)
Expected: Clear decision (full/gradual rollout, keep champion, or extend test) with justification and action items.
On failure: 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.
Validation
Common Pitfalls
- 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 before 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: Ensure outcome metrics correctly attributed to model predictions (not other system changes)
Related Skills
deploy-ml-model-serving - Model deployment infrastructure and versioningmonitor-model-drift - Ongoing performance monitoring post-rollout