llama-farm ML Use Cases Skill
Reference implementations for common ML patterns built on LlamaFarm.
When to Load
Load this skill when the user:
- Asks for an end-to-end ML example
- Wants to build a monitoring pipeline
- Asks about fraud detection patterns
- Needs a document processing pipeline
- Wants to combine multiple ML features
- Asks "how do I use anomaly detection for X?"
Use Case Catalog
| Use Case | ML Features Used | Complexity |
|---|---|---|
| IoT Monitoring | Polars buffers → Rolling features → Streaming anomaly | Medium |
| Fraud Detection | Streaming pre-screen → Batch analysis → Ensemble | High |
| Document Intelligence | OCR → NER → Classification → RAG | Medium |
Pattern Selection Guide
- Real-time monitoring? → IoT Monitoring pattern
- Transaction scoring? → Fraud Detection pattern
- Document processing? → Document Intelligence pattern
- Custom pipeline? → Combine patterns as building blocks
Quick Reference
IoT Monitoring Pipeline
Raw metrics → Polars buffer → Rolling features → Streaming ecod → Alerts
Fraud Detection Pipeline
Transaction → Streaming pre-screen → Daily batch ensemble → Risk score
Document Intelligence Pipeline
Document → OCR → NER → Classification → RAG index → Query
Progressive Disclosure
For detailed implementations:
iot-monitoring.md- Complete IoT pipeline with Polars + streaming anomalyfraud-detection.md- Hybrid real-time + batch fraud scoringdocument-intelligence.md- OCR → NER → classification → RAG pipeline