How Billion-User Recommendation Systems Are Built скачать в хорошем качестве

How Billion-User Recommendation Systems Are Built

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. 👉 Like 👍 if this helped 👉 Subscribe for more large-scale AI system design 👉 Comment what use-case you want next—e-commerce, OTT, or social platforms? #RecommendationSystem #MachineLearning #AIArchitecture #DataScience #MLOps #Personalization #BigData #AIEngineering #GenAI #DeepLearning #RecommenderSystems #TechExplained #MLSystems