Build a deployment checklist for a student ML project

A deployment checklist should block unsupported claims. Passing model evaluation is necessary, but a service also needs schema, artifact, error, monitoring and rollback evidence. “Runs on my laptop” is a development milestone.

Ten of twelve gates pass

The local deployment lab scores a teaching project against 12 binary evidence items.

from deployment_cases import checklist_case

result = checklist_case()
assert result["passed"] == 10
assert result["total"] == 12
assert result["release_ready"] is False
print(result["missing"])

The missing items are container_build_and_scan and production_load_test. A reviewed Dockerfile and in-process timing do not satisfy them, so release readiness remains false.

Cover the whole chain

The passing items include decision owner, data/split manifest, baseline/test report, schema tests, reload equivalence, error contract, artifact hash, monitoring policy, rollback runbook and privacy review. In a real project, each Boolean should link to evidence and reviewer status.

Add authentication, authorization, secrets, dependency scanning, accessibility and compliance requirements as the context demands. Do not copy one generic checklist into a high-stakes domain without domain review.

Present honest portfolio status

A student project can be strong as a reproducibly local service. Label unexecuted deployment work clearly. Include commands, sample requests, test outputs, limitations and next gates. Avoid “production-ready” when container, security or load evidence is absent.

The Data Science course rewards complete evidence and honest readiness status rather than deployment vocabulary.

Exercise

Turn every checklist item into a linkable artifact. Have another learner reproduce the service, then record pass, fail or blocked with evidence and owner.

Continue learning

This article is part of the Model deployment and MLOps sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Secure model artifacts and avoid untrusted deserialization.
• Continue with Hand over a model service with operational ownership.

Reference: Google Rules of ML.