ai-ml-engineering
CommunityProduction-ready ML reviews for AI systems.
Authorcamilooscargbaptista
Version1.0.0
Installs0
System Documentation
What problem does it solve?
AI/ML systems often lack formal production-readiness reviews, causing undetected risks at deployment and in production observability.
Core Features & Use Cases
- Evaluation Frameworks: Define metrics beyond accuracy (latency, reliability, safety, and governance).
- Monitoring & Drift: Establish production monitoring, drift detection, and alerting for deployed models.
- Use Case: Use this framework to validate model serving pipelines, MLOps workflows, and responsible-AI requirements before release.
Quick Start
Provide a concise production-readiness review for your current AI/ML system by outlining deployment target, lifecycle phase, and key evaluation criteria.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: ai-ml-engineering Download link: https://github.com/camilooscargbaptista/cto-toolkit/archive/main.zip#ai-ml-engineering Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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