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.

Reference architecture at a glance

flowchart TD
    A["Developer and experimentation layer"] --> B["Platform control plane"]
    B --> C["Data and feature layer"]
    B --> D["Compute runtime layer"]
    C --> D
    D --> E["Observability and operations"]
    B --> F["Release policy and environment contracts"]
    F --> D
    E --> B

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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

What makes this useful

• docs/architecture.md describes the logical platform layers and the interfaces worth versioning.
• docs/operating-model.md captures ownership, standards, and the human operating model around the platform.
• docs/release-process.md connects artifact promotion, checks, and rollback expectations.
• examples/environment-contract.json and examples/platform-topology.yaml make the patterns concrete enough to adapt.

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

Recommended reading path

1. Start with docs/architecture.md for the system model.
2. Read docs/operating-model.md for ownership and platform discipline.
3. Review docs/release-process.md plus the example contracts for how delivery controls become enforceable.