The episode examines the critical factors influencing machine learning (ML) performance on System-on-Chip (SoC) edge devices, moving beyond simplistic metrics like TOPS. It emphasizes that real-world ML efficacy hinges on a complex interplay of elements, including the SoC's compute and memory architectures, its compatibility with various ML model types, and the efficiency of data ingestion and pre/post-processing pipelines. Furthermore, the paper highlights the crucial roles of the software stack, power and thermal constraints, real-time behavior, and developer tooling in optimizing performance. Ultimately, it advocates for holistic performance evaluation using practical metrics like inferences per second and per watt, rather than just peak theoretical capabilities.