Design AI-Powered Uber System | Ride Booking | ML Driver Matching, Surge Pricing, ETA Prediction

Design a complete AI-Powered Ride Booking System like Uber in this full system design lecture.

In this video, we cover how modern ride-hailing platforms use Machine Learning, Real-time Streaming, and Distributed Systems to match riders with the best drivers — not just the nearest one.

You’ll learn:

✅ AI Driver Matching (ML ranking vs nearest driver)
✅ Real-time Feature Store (Redis + Streaming)
✅ Demand Prediction & Smart Driver Dispatch
✅ Dynamic Surge Pricing (ML-based)
✅ ETA Prediction using ML
✅ Fraud Detection & Cancellation Prediction
✅ End-to-End ML Inference Flow (under 50ms latency)
✅ Real-time Streaming with Kafka
✅ Multi-Region AI Architecture
✅ Scaling ML inference for millions of users
✅ Database choices (MySQL, Cassandra, Redis)
✅ Observability & A/B testing for ML models

We also discuss latency budgets, scaling numbers, and production trade-offs expected in senior system design interviews.

This video is useful for:
• Senior Software Engineer interviews
• Staff / Principal Engineer interviews
• System Design preparation
• Machine Learning System Design
• Distributed Systems learning

Видео Design AI-Powered Uber System | Ride Booking | ML Driver Matching, Surge Pricing, ETA Prediction канала Learning With Chetna