ml-system-design
Skill0 starsChanged 5 months agoMITView on GitHub
Design or review an end-to-end ML/AI system across data, training, serving, MLOps, and scale — including feature stores, model serving, evaluation, and feedback loops. Use when the user asks to "design a recommendation/ranking system", review an ML pipeline, or discuss drift, model serving, or evaluation. For RAG specifically use rag-system-design; for LLM apps use llm-app-architecture; for generic (non-ML) systems use architecture-review.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- dathuynh1108/agent-skillstheir own version, first seen Oct 11, 2026
- BejeweledMe/codex-pro-agent-skillstheir own version, first seen Sep 6, 2026
Other versions2 projects use a version of this.
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Wrote their own version · 2
- BejeweledMe/codex-pro-agent-skills★ 7same name
- dathuynh1108/agent-skills★ 4same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository12 other files it gives its agents.
- adr-writerSkill
- architecture-reviewSkill
- gcp-architectureSkill
- incident-postmortemSkill
- interview-modeSkill
- leadership-lensSkill
- learning-coachSkill
- llm-app-architectureSkill
- mlops-pipeline-reviewSkill
- observability-reviewSkill
- principal-code-reviewSkill
- rag-system-designSkill
The file
Files
ML System Design Review
Evaluate every stage. Tie each choice back to a product/latency/cost constraint.
Data
- Collection & sources
- Quality & validation
- Labeling strategy
- Versioning & lineage
Training
- Feature engineering / feature store
- Model training & reproducibility
- Experiment tracking
Serving
- Online inference (latency budget)
- Batch inference
- Model registry & versioning
Operations
- Drift detection (data + concept)
- Monitoring & alerting
- Rollback / shadow / canary
Scale & cost
- P95 latency
- Throughput
- Cost per prediction
Always discuss
- Feature store
- Evaluation methodology (offline + online)
- Feedback loops & retraining triggers
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
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- It worked
- Partly
- It didn't
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