Deployment & Serving

Building an AI model that works in a notebook is one thing. Running it reliably in production, serving thousands or millions of requests, keeping costs under control, and maintaining quality over time is something else entirely. Deployment and serving is where AI meets the real world, and it's where many organisations struggle most. The gap between a working prototype and a production system is sometimes called the "last mile" of machine learning, and it's often the hardest mile. Production AI systems need to handle variable load, recover from failures gracefully, respond within acceptable latency bounds, and do all of this cost-effectively. The infrastructure choices you make at deployment - cloud versus on-premises, real-time versus batch, centralised versus edge - have long-lasting implications for cost, performance, and flexibility. This area has matured considerably in recent years, with better tooling, managed services, and established patterns. But it still requires careful thought about your specific requirements, because the right deployment architecture depends heavily on your use case, scale, latency needs, and budget.