In most AI research today, deep neural networks (DNNs) are designed solely to improve prediction accuracy, often ignoring real-world constraints such as compute and memory requirements. Embedded vision developers typically prefer to use these state-of-the-art (SOTA) DNNs from the research literature due to the costs and expertise needed to develop new models. However, these SOTA DNNs are typically too resource-hungry to be run on embedded processors. Neural architecture search (NAS) is an effective approach to bridge the gap between optimal network design and efficient deployment. In this presentation, we will explain the principles of NAS. We will show how NAS can enable efficient computer vision at the edge by considering deployment aspects (e.g., the efficiency of quantized operators) to derive tailor-made solutions for a given edge node, and how to address NAS scalability via smart search space design and efficient performance estimation selection.