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From Jupyter Notebook to Production: ML Deployment Guide

January 1, 1970
15 min read
MLOpsDeploymentDevOps

The real gap

The hard part isn’t training a model. It’s making inference reliable, observable, and cheap.

Minimal production checklist

  • Deterministic preprocessing (versioned)
  • Model + feature versioning
  • Canary + rollback
  • Monitoring: latency, error rate, drift proxies
  • Automated evaluation on fresh data