ML Platform Reference

A sanitized reference repository for ML platform engineering patterns: environment contracts, release gates, operating model artifacts, and platform documentation structure.

This repo is intentionally lightweight and framework-agnostic. It is meant to demonstrate engineering design and operating discipline, not prescribe a single vendor stack.

What this includes

• Platform architecture documentation (control plane, data plane, workload runtime)
• Environment and release contracts for ML workloads
• Model promotion checklist and release-gate examples
• Operating model guidance (ownership, RFCs, SLOs, incident handling)

Who this is for

• ML platform engineers and MLOps engineers
• Engineering leads designing platform standards
• Teams building repeatable ML delivery workflows

Repository layout

ml-platform-reference/
├── docs/
│   ├── architecture.md
│   ├── operating-model.md
│   └── release-process.md
└── examples/
    ├── environment-contract.json
    ├── model-promotion-checklist.md
    └── platform-topology.yaml

Design principles

1. Treat platform interfaces as products (versioned contracts, clear ownership).
2. Make release decisions explicit (metrics + policy + audit trail).
3. Default to reproducibility (immutable build inputs, environment parity).
4. Optimize for operability (observability, runbooks, rollback paths).
5. Keep business logic separate from platform concerns.

Related repos

• ml-release-gates: example CLI for policy-driven model promotion decisions
• feature-pipeline-quality: dataset contract checks and quality reporting