Backtesting ML pipelines before rollout

Backtesting bridges the gap between offline metrics and production behavior. It prevents surprises by replaying real workloads through new code and models.

Ingredients of a good backtest

• Golden datasets: curated inputs with expected outputs for core user journeys.
• Replay harness: stream historical traffic with time travel and deterministic feature pipelines.
• Failure injection: simulate nulls, outages, and schema drifts to validate resilience.

When to run it

• Before every canary rollout and when dependencies change (feature store schemas, upstream services).
• As part of incident postmortems to codify new regression checks.
• On schedule for critical services (daily/weekly) with alerts when deltas breach thresholds.

Related reading

• Guardrails to pair with backtests: Platform guardrails that keep ML services shippable.
• Ads angle: Ads ML as a subtopic of production ML systems.
• Pillar hub: Practical MLOps.

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