ml-pipeline

Design ML Pipeline

Design an end-to-end ML system architecture for a given problem.

Arguments

$ARGUMENTS - The ML problem to design for (e.g., "recommendation system", "fraud detection", "search ranking", "content moderation")

Workflow

1. Clarify requirements by understanding:

   • What predictions are being made?
   • What is the latency requirement? (real-time vs batch)
   • What is the scale? (QPS, data volume)
   • Who are the consumers of predictions?

2. Load relevant skills based on the problem:

   • Core ML architecture → ml-system-design
   • LLM-based systems → llm-serving-patterns
   • RAG systems → rag-architecture
   • Inference optimization → ml-inference-optimization
   • Vector search → vector-databases

3. Spawn the ml-systems-designer agent for comprehensive design:

   • Use Task tool with subagent_type="ml-systems-designer"
   • Provide full problem context and requirements
   • Request end-to-end architecture

4. Design the complete pipeline:

   • Data ingestion and processing
   • Feature engineering and feature store
   • Model training infrastructure
   • Model serving and inference
   • Monitoring and observability
   • A/B testing and experimentation

5. Address cross-cutting concerns:

   • Training-serving skew prevention
   • Feature consistency
   • Model versioning and rollback
   • Cost optimization

Example Usage

/sd:ml-pipeline recommendation system for 100M users
/sd:ml-pipeline real-time fraud detection for payments
/sd:ml-pipeline search ranking for e-commerce with 10M products
/sd:ml-pipeline content moderation for social media
/sd:ml-pipeline ad click prediction at 1M QPS
/sd:ml-pipeline customer churn prediction
/sd:ml-pipeline demand forecasting for inventory

Problem Categories

Category	Key Considerations
Recommendations	Cold start, real-time signals, A/B testing
Fraud/Risk	Low latency (<100ms), rules + ML hybrid, feedback loops
Search/Ranking	Multi-stage ranking, personalization, position bias
NLP/LLM	Inference cost, caching, streaming responses
Computer Vision	GPU inference, batching, edge deployment
Time Series	Feature freshness, windowing, seasonal patterns

Output

A comprehensive ML system architecture including:

• High-level architecture diagram (component-based)
• Data flow from sources to predictions
• Technology stack recommendations
• Trade-offs and alternatives considered
• Phased implementation approach
• Cost and scale considerations