ml-engineer - Production ML systems workflow
Guides production machine-learning system work covering PyTorch 2.x, TensorFlow, model serving, feature engineering, A/B testing, monitoring, and provenance-aware workflows.
Tags
Updated: 2026-10-03Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Build production ML systems
- Design model serving architectures
- Engineer ML features
- Implement A/B testing
- Monitor model performance and drift
- Validate outcomes and verification
- Preserve workflow provenance
- Load relevant support files
Inputs
- User goals and constraints
- Required task inputs
- Repository context
- metadata.json and ORIGIN.md
- Copied upstream references, examples, or scripts
Outputs
- Actionable workflow guidance
- Verification results
- Provenance-grounded review notes
- Explicit skill handoff decisions