# Ml Pipeline > Design an ML system for a problem Use when this capability is needed. - Skill: `tomevault-io/ml-pipeline-3` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ml-pipeline-3` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ml-pipeline-3/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ml-pipeline-3 --- # 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 ```bash /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 --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/melodic-software) — claim your Tome and manage your conversions.