post://production-ml-inference-at-scale

Production ML Inference at Scale: Design Patterns and Pitfalls

author: Swadhin Biswas • • read: 1 min
Machine LearningSystem DesignBackend
Production ML Inference at Scale: Design Patterns and Pitfalls

Production ML Inference at Scale

Building reliable inference is not only about model quality; it is about availability, latency, and cost.

Core design

  • Online feature store for low-latency reads
  • Canary rollout for model versions
  • Graceful fallback to previous model

Latency budget

{
  "type": "bar",
  "data": {
    "labels": ["Gateway", "Feature Fetch", "Model", "Post-Processing"],
    "datasets": [{ "label": "ms", "data": [8, 24, 30, 10], "backgroundColor": "#7aa2ff" }]
  }
}

Key equation

$$ ext{p95 total latency} approx sum_i ext{p95}(stage_i) $$

Use strict SLO alerts and auto-rollback when error budget burn rate rises.