A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
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Updated
Jun 7, 2025 - Jupyter Notebook
A modular, explainable recommendation pipeline leveraging multiple strategies—collaborative filtering, embeddings, and fallback logic—for robust, personalized product recommendations in real-world scenarios.
A self-healing text classification pipeline built with LangGraph and a fine-tuned DistilBERT (LoRA) model. The system uses prediction confidence to decide whether to accept, reject, or trigger a fallback (user clarification or alternative strategy). Designed with a clean CLI, structured logging, and human-in-the-loop reliability. Demo video:
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Production-ready Notion MCP wrapper with health monitoring, auto-reconnect & fallback. OpenClaw skill.
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FastAPI AI platform service for model routing, health-aware fallback, latency and quality thresholds, cost-aware selection, automated tests, and Docker support.
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