Day 51: Building a Production-Ready Spam Detection System
What We’ll Build Today
End-to-end spam classifier using logistic regression on real email data
Feature engineering pipeline that extracts patterns from raw text
Model evaluation framework measuring precision, recall, and F1-scores
Production deployment simulation showing how Gmail processes 100M+ emails daily
Why This Matters: From Classroom to 100 Million Emails Per Second
Every second, Gmail’s spam filters analyze over 100 million emails. Behind this massive operation sits the same logistic regression you learned on Day 49. The difference? Production systems add sophisticated feature engineering, real-time inference pipelines, and continuous model updates.
Today you’ll build a complete spam detection system that mirrors the architecture used at Google, Microsoft, and Yahoo. You’re not building a toy - you’re implementing the exact patterns that protect billions of inboxes worldwide. The code you write today could scale to production with surprisingly few changes.
Core Concept: The Spam Detection Pipeline Architecture
