How Billion-User Recommendation Systems Are Built

Описание: In this video, we break down how enterprise-scale recommendation engines are designed and deployed—the same type of systems used by big e-commerce, streaming, and social media platforms.

You’ll learn:
👉 How user events (click, view, cart, buy) are collected
👉 Feature engineering & feature stores
👉 Embeddings & ANN search
👉 Candidate generation strategies
👉 Ranking models
👉 Real-time vs batch pipelines
👉 Personalization at massive scale
👉 End-to-end system architecture

📌 Topics Covered:

Event tracking pipelines

Offline vs online features

Feature stores

User & item embeddings

Approximate nearest neighbor search

Candidate generation

Ranking models

Exploration vs exploitation

A/B testing

Monitoring & feedback loops

Perfect for:
✔️ ML engineers
✔️ Data scientists
✔️ Backend engineers
✔️ GenAI builders
✔️ Product leaders
✔️ Startup founders

If you’re learning recommendation systems, MLOps, large-scale ML systems, personalization engines, or production ML, this video gives you a complete enterprise-level mental model.

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