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Machine Learning Model Deployment Checklist

Hard 17 items · 2 hours
testuser's avatar
testuser Published 2 weeks ago
Ml Deployment

A practical step-by-step checklist to deploy ML models reliably into production. Ideal for ML engineers, data scientists, and SREs who want repeatable, observable, and safe model rollouts. Covers versioning, APIs, validation, benchmarking, monitoring, testing, and rollback planning.

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  1. Pin and document the model version — Tag artifacts with semantic version, git commit, training data snapshot, and config.
  2. Choose and fix a serialization format — Pick a portable format (ONNX, SavedModel, TorchScript) and document input/output schema.
  3. Store the serialized artifact in an immutable registry — Save artifacts to a read-only model registry or object store with metadata.
  4. Containerize runtime and pin dependencies — Build a Docker image, pin package versions, and include hardware specs (CPU/GPU).
  5. Expose the model via a versioned REST API — Provide /predict, /health, and /version endpoints and include model version in responses.
  6. Implement input validation and sanitization — Validate schema, types, ranges and reject or normalize bad inputs before inference.
  7. Add authentication, rate limiting, and request limits — Require tokens, enforce quotas, and set max payload size to protect the service.
  8. Benchmark inference latency and throughput — Measure p50/p95/p99 latency and max QPS with production-like payloads.
  9. Set performance thresholds and automated alerts — Define alert rules for latency, error rate, resource saturation, and throughput drops.
  10. Implement logging and structured tracing — Log request id, model version, inputs/outputs (mask PII), and propagate trace IDs.
  11. Monitor data and concept drift against a baseline — Track feature distributions, prediction shifts, and label changes over time.
  12. Set up canary or A/B testing deployment — Deploy candidate model to a subset of traffic to validate performance vs. control.
  13. Route a small percentage of traffic to the candidate model — Start with 1–5% traffic and increase gradually while monitoring metrics.
  14. Compare model and business metrics; define pass/fail criteria — Compare accuracy, latency, error rates, and key business KPIs; document thresholds.
  15. Automate CI/CD for model builds, tests, and rollouts — Trigger builds from model registry tags, run smoke tests, and automate promotion.
  16. Prepare rollback and hotfix strategy — Keep previous artifact ready, script fast rollback, and define automated rollback triggers.
  17. Run end-to-end validation in shadow mode with production-like data — Validate responses without impacting users to catch edge cases before routing traffic.
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