The three silent killers in edge AI deployment, and how to catch them before they catch you.

The article identifies three root causes of edge AI deployment failures: misleading x86 profiling numbers when targeting ARM devices, memory architecture differences between development and edge hardware, and runtime behavior divergences across platforms. It argues that standard ML tooling is built for cloud deployment and leaves a critical gap at the edge, where latency and memory estimates from dev machines can be 2–5x off from real device performance. The author advocates for proper pre-deployment profiling workflows that test directly on target hardware.