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From ml-engineering
Serve models reliably: choose online vs batch by the use case, deploy a versioned model from the registry, optimize latency to a budget (batching/quantization/distillation/hardware), and roll out safely with shadow -> canary -> full.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ml-engineering:model-servingThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Real-time per request -> **online endpoint**. Periodic scoring of many records -> **batch**. Don't mismatch.
Real-time per request -> online endpoint. Periodic scoring of many records -> batch. Don't mismatch.
Deploy a specific registered version with known lineage — not a copied file. Promotion goes through the registry gate.
Batching, quantization/distillation, right hardware, caching — to a budget. Measure.
Shadow (compare on live traffic, no user impact) -> canary (a slice) -> full, promote on the metric. Blind swaps are silent regressions.
npx claudepluginhub mcorbett51090/ravenclaude --plugin ml-engineeringGuides completion of development work by verifying tests, detecting environment, and presenting structured options for merge, PR, or cleanup.
Enforces test-driven development: write failing test first, then minimal code to pass. Use when implementing features or bugfixes.
Guides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.