About
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
name: mle-workflow description: Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks. origin: ECC
Machine Learning Engineering Workflow
Use this skill to turn model work into a production ML system with clear data contracts, repeatable training, measurable quality gates, deployable artifacts, and operational monitoring.
When to Activate
- Planning or reviewing a production ML feature, model refresh, ranking system, recommender, classifier, embedding workflow, or forecasting pipeline
- Converting notebook code into a reusable training, evaluation, batch inference, or online inference pipeline
- Designing model promotion criteria, offline/online evals, experiment tracking, or rollback paths
- Debugging failures caused by data drift, label leakage, stale features, artifact mismatch, or inconsistent training and serving logic
- Adding model monitoring, canary rollout, shadow traffic, or post-deploy quality checks
Scope Calibration
Use only the lanes that fit the system in front of you. This skill is useful for ranking, search, recommendations, classifiers, forecasting, embeddings, LLM workflows, anomaly detection, and batch analytics, but it should not force one architecture onto all of them.
- Do not assume every model has supervised labels, online serving, a feature store, PyTorch, GPUs, human review, A/B tests, or real-time feedback.
- Do not add heavyweight MLOps machinery when a data contract, baseline, eval script, and rollback note would make the change reviewable.
- Do make assumptions explicit when the project lacks labels, delayed outcomes, slice definitions, production traffic, or monitoring ownership.
- Treat examples as interchangeable scaffolds. Replace metrics, serving mode, data stores, and rollout mechanics with the project-native equivalents.
Related Skills
python-patternsandpython-testingfor Python implementation and pytest coveragepytorch-patternsfor deep learning models, data loaders, device handling, and training loopseval-harnessandai-regression-testingfor promotion gates and agent-assisted regression checksdatabase-migrations,postgres-patterns, andclickhouse-iofor data storage and analytics surfacesdeployment-patterns,docker-patterns, andsecurity-reviewfor serving, secrets, containers, and production hardening
Reuse the SWE Surface
Do not treat MLE as separate from software engineering. Most ECC SWE workflows apply directly to ML systems, often with stricter failure modes:
The recommended minimal --with capability:machine-learning install keeps the core agent surface available alongside this skill. For skill-only or agent-limited harnesses, pair skill:mle-workflow with agent:mle-reviewer where the target supports agents.
| SWE surface | MLE use |
|-------------|---------|
| product-capability / architecture-decision-records | Turn model work into explicit product contracts and record irreversible data, model, and rollout choices |
| repo-scan / codebase-onboarding / code-tour | Find existing training, feature, serving, eval, and monitoring paths before introducing a parallel ML stack |
| plan / feature-dev | Scope model changes as product capabilities with data, eval, serving, and rollback phases |
| tdd-workflow / python-testing | Test feature transforms, split logic, metric calculations, artifact loading, and inference schemas before implementation |
| code-reviewer / mle-reviewer | Review code quality plus ML-specific leakage, reproducibility, promotion, and monitoring risks |
| build-fix / pr-test-analyzer | Diagnose broken CI, flaky evals, missing fixtures, and environment-specific model or dependency failures |
| quality-gate / test-coverage | Require automated evidence for transforms, metrics, inference contracts, promotion gates, and rollback behavior |
| eval-harness / verification-loop | Turn offline metrics, slice checks, latency budgets, and rollback drills into repeatable gates |
| ai-regression-testing | Preserve every production bug as a regression: missing feature, stale label, bad artifact, schema drift, or serving mismatch |
| api-design / backend-patterns | Design prediction APIs, batch jobs, idempotent retraining endpoints, and response envelopes |
| database-migrations / postgres-patterns / clickhouse-io | Version labels, feature snapshots, prediction logs, experiment metrics, and drift analytics |
| deployment-patterns / docker-patterns | Package reproducible training and serving images with health checks, resource limits, and rollback |
| canary-watch / dashboard-builder | Make rollout health visible with model-version, slice, drift, latency, cost, and delayed-label dashboards |
| security-review / security-scan | Check model artifacts, notebooks, p
