A/B Testing ML Models
Shipping a new model version without A/B testing is gambling. We build experimentation infrastructure that routes traffic between model variants, tracks business metrics per variant, and determines statistical significance — so you know which model actually performs better, not which one feels better.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Traffic Routing Architecture
Traffic splitting happens at the service mesh level (Istio, Linkerd) or within the model serving layer (KServe, Seldon). We implement consistent hashing so the same user always hits the same model variant — critical for coherent user experiences. Traffic percentages are adjustable without redeployment via ConfigMap or feature flag updates.
Metric Collection & Analysis
Each model variant logs predictions with experiment IDs. Business metrics (click-through rate, conversion, revenue per user) get attributed to the correct variant. We configure statistical significance calculations — sequential testing for early stopping or fixed-horizon tests with proper sample size estimates. No peeking at results and calling it early.
Guardrail Metrics
Beyond the primary metric, guardrail metrics catch regressions: latency, error rate, and secondary business metrics must not degrade. If a variant improves conversion but doubles latency, the guardrails flag it. Automated rollback triggers when any guardrail metric crosses a threshold, even if the primary metric looks good.
Experiment Management
We set up an experiment registry tracking all active and completed tests — which model versions, traffic splits, start/end dates, and outcomes. Post-experiment reports generate automatically with confidence intervals and effect sizes. Your team builds institutional knowledge about what model changes actually move the needle.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.