"AI is becoming ubiquitous, powering everything from self-driving cars to nearly every application on mobile devices. However, many ML models are not well-suited for edge devices because they were trained in the cloud and do not fit those devices well. Such a mismatch greatly hinders the potential of AI and has been a universal barrier for companies deploying powerful ML models to resource-constrained devices at the edge.

In this talk, OmniML will discuss the challenges of adapting, optimizing, and deploying machine learning models on resource-constrained devices at the edge. We will go over industry use cases that OmniML has encountered working with customers in electric vehicle manufacturing, advanced driver assistance systems (ADAS), robotics, IoT smart cameras, and other verticals. We will explore some of the problems customers encounter when trying to fit advanced computer vision onto these edge devices and how OmniML is using our Omnimizer MLOps platform to help customers solve their current pain points of adapting ML models to fit their business needs."