First Principles of MLOps

Tools change; these principles don't. Think from first principles when designing or reviewing ML systems.

Reproducibility

Same inputs, same outputs — every time.

Environments, code, data, and model artifacts must be versioned and replayable. If you can't reproduce a run, you can't debug or improve it.

Automation

Remove manual steps; reduce human error.

Build, test, deploy, and monitor via pipelines. Manual handoffs and one-off scripts don't scale. Automate the path from commit to production.

Observability

You can't fix what you can't see.

Logs, metrics, traces, and model/feature drift signals. Know when things break or degrade before users do. Define SLOs and alert on them.

Versioning

Code, data, and models are first-class versioned assets.

Track which model, which dataset, and which code produced a given outcome. Versioning enables rollback, audit, and reproducible experiments.

Governance & Safety

Control who can change what, and what gets to production.

Access control, approval gates, and compliance. Production changes should be auditable and gated. Model risk and fairness belong in the loop.

Environment Separation

Local → Dev → Staging → Prod. Don't skip steps.

Clear boundaries between environments prevent "works on my machine" and protect production. Each stage has a purpose: build, integrate, validate, run.

These principles map to the pipeline: Environments (Local → Prod), Learning Paths, and Roadmap.