mle-workflow - Production Machine Learning Engineering Workflow
Guides production ML system work across data contracts, reproducible training, evaluation, deployment, monitoring, and rollback.
Tags
Updated: 2026-10-16Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Define data contracts
- Build reproducible training
- Evaluate model quality
- Package model artifacts
- Design inference paths
- Plan model rollouts
- Monitor production models
- Define rollback paths
Inputs
- ML project requirements
- Training data
- Labels and features
- Model evaluation criteria
- Serving requirements
- Deployment constraints
- Monitoring ownership
Outputs
- Data and metric contracts
- Training and evaluation plans
- Versioned model artifacts
- Prediction endpoints or batch jobs
- Rollout plans
- Monitoring dashboards
- Rollback procedures
- Error analysis reports