Machine Learning Model Deployment Checklist

17 items · Ml Deployment · Hard difficulty · 2 hours

Deploy ML models safely with a practical, production-ready checklist.

• Pin and document the model version

  Tag artifacts with semantic version, git commit, training data snapshot, and config.

• Choose and fix a serialization format

  Pick a portable format (ONNX, SavedModel, TorchScript) and document input/output schema.

• Store the serialized artifact in an immutable registry

  Save artifacts to a read-only model registry or object store with metadata.

• Containerize runtime and pin dependencies

  Build a Docker image, pin package versions, and include hardware specs (CPU/GPU).

• Expose the model via a versioned REST API

  Provide /predict, /health, and /version endpoints and include model version in responses.

• Implement input validation and sanitization

  Validate schema, types, ranges and reject or normalize bad inputs before inference.

• Add authentication, rate limiting, and request limits

  Require tokens, enforce quotas, and set max payload size to protect the service.

• Benchmark inference latency and throughput

  Measure p50/p95/p99 latency and max QPS with production-like payloads.

• Set performance thresholds and automated alerts

  Define alert rules for latency, error rate, resource saturation, and throughput drops.

• Implement logging and structured tracing

  Log request id, model version, inputs/outputs (mask PII), and propagate trace IDs.

• Monitor data and concept drift against a baseline

  Track feature distributions, prediction shifts, and label changes over time.

• Set up canary or A/B testing deployment

  Deploy candidate model to a subset of traffic to validate performance vs. control.

• Route a small percentage of traffic to the candidate model

  Start with 1–5% traffic and increase gradually while monitoring metrics.

• Compare model and business metrics; define pass/fail criteria

  Compare accuracy, latency, error rates, and key business KPIs; document thresholds.

• Automate CI/CD for model builds, tests, and rollouts

  Trigger builds from model registry tags, run smoke tests, and automate promotion.

• Prepare rollback and hotfix strategy

  Keep previous artifact ready, script fast rollback, and define automated rollback triggers.

• 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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