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Hyper-Personalization Done the Pythonic Way

Reality Check

Saying “Hello,qasi” isn’t personalization anymore. Real-time, context-aware recommendations are what really convert.

The Pythonic Solution

  • Tools: FastAPI, Faiss vector search, Redis streams

  • No proprietary black boxes — just fast, flexible, open tools

Key Takeaways

By the end of this guide, you’ll understand how to:

  • Design statistically sound experiments using Jupyter notebooks and scipy.stats

  • Build A/B testing workflows that integrate easily with your existing Python web apps

  • Deploy experiments at scale using feature flags and rollout scripts

  • Track, analyze, and visualize results for clear business impact

What You'll Learn

  • How to embed product catalogs using sentence-transformers

  • Perform sub-50ms similarity look-ups with Faiss + NumPy

  • Build privacy-safe feature stores using DuckDB and Parquet

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