AI System Design and Architecture
For backend and ML engineers who need to take a model from a single server to a production service that stays fast, affordable and available under real traffic. You will be able to choose service boundaries, size queues, fleets and caches with queueing arithmetic, and design APIs, timeouts and rollouts that survive failures.
Course Content
Decide how to cut an AI system into deployable pieces, keep it stable under bursty load with queues, balancers and autoscalers, and serve models efficiently with batching and sharding. 3 lessons, about 45 minutes.
Build the service layer around a model: API contracts that survive change, a latency budget you can defend, caching that pays, and the timeouts, breakers and health checks that keep one failure from becoming an outage. 3 lessons, about 45 minutes.
Rebuild a fragile single-box claims processor as a fault-tolerant inference platform, and defend every design choice with capacity, latency and cost arithmetic. 1 lesson, about 15 minutes.