AnServApp program
2020
With the rise of deep learning, many applications can benefit by moving from servers closer to the users -on mobile and edge devices, reducing network transfer, bandwidth, latency, and ensuring better privacy. And this comes with its challenges, ranging from running a computationally expensive algorithm on resource constraint devices, to large memory, battery and power requirements, to missing access to user data needed for training models, all the way to large scale deployment and A/B testing AI models, not on your infrastructure. In this demo-based talk, we will take a 360-degree look at the mobile AI application industry, by dive into the development lifecycle, identifying challenges, benchmarking, and solving them step by step. By the end, the practical learning should be applicable beyond mobile applications, helping optimize both costs, bandwidth, and compute on a range of devices.