Build a Model-Serving Engine

• ✓Build a model-serving engine from scratch — the prediction API, batching queue, and cache behind a production inference service
• ✓Measure a server honestly with p50/p99 latency and throughput under real concurrency, not a single-request stopwatch
• ✓Implement adaptive batching that trades tail latency for throughput against an explicit SLO
• ✓Add a prediction cache, measure its hit rate, and recognise the requests that must never be cached
• ✓Serve two model versions side by side and shift traffic with shadow and canary routing
• ✓Keep a server alive under overload with queue limits, timeouts, and load shedding
• ✓Produce a capacity plan — the requests per second one box sustains at a target p99, and the cost per 1,000 predictions

Build the inference layer every ML product runs on — the service that turns a trained model into an API that answers thousands of requests a second without falling over, and the measurements that prove it.