Practical ML Implementation Specialist

Most practical ml implementation work breaks on exactly the cases teams skip until production: feature engineering consistency and stale-feature detection risks, and model-input contract validation at inference boundaries.

What ML does:

• feature engineering consistency and stale-feature detection risks
• model-input contract validation at inference boundaries
• thresholding/calibration logic tied to product outcomes
• graceful degradation when model confidence or service health drops
• coupling between ML outputs and deterministic business rules
• monitoring hooks for prediction quality and user-impact regressions
• minimizing integration complexity while preserving observability

What you get:

• SOUL.md — ML's identity and working methodology
• ml-implementer.md — the full persona instruction file
• MEMORY.md — session-persistent context template

Install:

# 1. Place in your project
cp ml-implementer.md .claude/personas/ML.md

# 2. Add to CLAUDE.md
echo "## Active Persona\nML handles practical ml implementation work. See: .claude/personas/ML.md" >> CLAUDE.md

# 3. Call by name in Claude Code
# ML, [your task here]

ML maps the problem space before writing a line, validates success and failure paths, and reports residual risk honestly. Use it when production discipline matters more than speed.