ml-system-design
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deployment and monitoring.
npx skills add majiayu000/claude-skill-registry --skill ml-system-design --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
This skill provides frameworks for designing production machine learning systems, from data pipelines to model serving.
How it works
- Describes when to use the skill for designing end-to-end ML systems, feature store architecture, training pipelines, serving infrastructure, interview prep, and evaluating ML platform tools.
- Presents an ML system lifecycle with stages: Data Ingestion, Feature Pipeline, Feature Store, Model Training, Model Registry, Model Serving, and Monitoring, including supporting components like Data Lake, Online Store, and Metrics Store.
- Details feature store architecture with offline and online stores and a feature registry, plus feature types (Batch, Streaming, On-demand).
- Covers training infrastructure patterns (Single-node, Distributed data-parallel, Model-parallel, Hyperparameter tuning) and experiment tracking elements (Hyperparameters, Metrics, Artifacts, Code version, Environment, Data version).
- Outlines model serving patterns (Online REST/gRPC, Batch, Streaming, Embedded) and an example online serving architecture with a Load Balancer, API Gateway, multiple models, and a Feature Store.
- Provides latency optimization techniques (Batching, Caching, Quantization, Distillation, GPU inference) with their trade-offs.
- Includes A/B testing architecture, metrics to track (Model, Business, Guardrail, Segment), and statistical considerations (Sample size, Duration, Multiple testing, Early stopping).
- Describes model monitoring areas (Data quality, Feature drift, Prediction drift, Model performance, Operational) and a drift detection pipeline using tests like PSI, KS Test, and Chi-squared.
When to use it
Use this skill when designing end-to-end ML systems for production, planning feature store architecture, designing model training pipelines, planning model serving infrastructure, preparing for ML system design interviews, or evaluating ML platform tools and frameworks.
What it can touch
- Allowed tools: Read, Glob, Grep
- References to components and tools include Data Ingestion (Kafka, Kinesis, Pub/Sub), Feature Store (Feast, Tecton, Vertex AI), Model Training (SageMaker, Vertex AI, Kubeflow), Model Serving (TensorFlow Serving, Triton, vLLM), Monitoring tools (Evidently, WhyLabs, Arize).
- Architecture diagrams and text blocks illustrate how components interact, and example configurations imply integration of a Feature Store with Online and Offline stores, a Model Registry, and a Monitoring system.
Caveats
- License stated as MIT.
- Declared tools: claude-code.
- The content emphasizes framework-level guidance and architectural patterns without promising specific outcomes.
# ML System Design This skill provides frameworks for designing production machine learning systems, from data pipelines to model serving. ## When to Use This Skill **Keywords:** ML pipeline, machine learning system, feature store, model training, model serving, ML infrastructure, MLOps, A/B testing ML, feature engineering, model deployment **Use this skill when:** - Designing end-to-end ML systems for production - Planning feature store architecture - Designing model training pipelines - Planning model serving infrastructure - Preparing for ML system design interviews - Evaluating ML platform tools and frameworks ## ML System Architecture Overview ### The ML System Lifecycle ```text ┌─────────────────────────────────────────────────────────────────────────┐ │ ML SYSTEM LIFECYCLE │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────────┐ │ │ │ Data │──▶│ Feature │──▶│ Model │──▶│ Model │──▶│ Monitor│ │ │ │ Ingestion│ │ Pipeline │ │ Training │ │ Serving │ │ & Eval │ │ │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ └────────┘ │ │ │ │ │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ │ │ ┌──────────┐ ┌──────────┐ ┌──────
- When to Use This Skill
- ML System Architecture Overview
- The ML System Lifecycle
- Key Components
- Feature Store Architecture
- Why Feature Stores?
- Feature Store Components
- Feature Types
- Training-Serving Consistency
- Model Training Infrastructure
- Training Pipeline Components
- Training Infrastructure Patterns
- Experiment Tracking
- Model Serving Architecture
What does the ml-system-design skill do?
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deployment and monitoring.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-system-design --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.