Edge computing opens up a new world of use cases for deep learning across numerous markets, including manufacturing, transportation, healthcare and retail. Edge deployments also pose new challenges for machine learning, not seen in cloud deployments. Constrained resources, tight latency requirements, limited bandwidth and unreliable networks require us to rethink how we build, deploy and operate deep learning models at the edge. In this presentation, we will introduce proven techniques, patterns and best practices for optimizing computer vision models for the edge. We’ll cover quantization, pruning, low-rank approximation and knowledge distillation, explaining how they work and when to use them. And we’ll touch on how your choice of ML framework and processor affect how you use these optimization techniques.