MLOps in 7 Artifacts: What You Must Produce to Ship Models

MLOps is not a tool. It’s a set of deliverables that turn ML into a maintainable product. If you want employability-level skills, learn to produce artifacts — not only notebooks.

The 7 artifacts (the real definition of ‘done’)

1) Data contract

• Schema (types, categories, ranges)
• Time semantics (timezone, frequency, missing policy)
• Unit conventions (°C vs K, kg vs lb)

2) Evaluation suite

• Golden test cases and edge cases
• Leakage-proof validation split
• Business-aligned metric + threshold policy

3) Reproducible training pipeline

• Pinned dependencies
• Single command to retrain
• Artifacts logged (model, params, metrics, dataset version)

4) Model registry & promotion workflow

• Register candidate versions
• Staging gate (evaluation + human review)
• Promotion to production + rollback plan

5) Deployment specification

• API schema (request/response)
• Latency budget and scaling assumptions
• Batch vs real-time decision

6) Monitoring plan

• Data drift + prediction drift
• Operational metrics (errors, latency)
• Alert thresholds and on-call owner

7) Documentation for audits and handovers

• Intended use + limitations
• Known failure modes
• Owner + SLA + incident response steps