Building Production-Ready Face Recognition at Scale: A Python and Docker Architecture

This approach demonstrates a practical architectural pattern—separating expensive computation from retrieval—that applies across industries from photography to surveillance and forensics. By containerizing the workload with Docker and optimizing for memory constraints, developers can deliver powerful machine learning features without imposing complex native dependencies on end users.