heissanjay

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By heissanjay ยท Published 1/18/2026

Evaluation-Driven ML Releases

A release workflow that treats eval quality gates as first-class deployment checks.

1 min read

  • Evaluation
  • Quality
  • Release Engineering

Production ML releases should pass quality gates the same way services pass CI checks.

Why eval gates matter

  • They catch silent regressions before users do.
  • They force teams to define measurable quality targets.
  • They make rollback decisions objective.

Release checklist

  1. Run offline benchmark suite.
  2. Compare against current production baseline.
  3. Validate latency and cost budgets.
  4. Gate deployment if metrics violate thresholds.

My Gear

Deep dive

How we define gate thresholds

Thresholds combine absolute quality floors and relative regression limits.
Example: block release if factuality drops more than 2% or latency increases more than 15%.