Don’t let data drift derail edge compute machine learning models

Edge computing has come of age, with deployments enabling many applications that process data from IoT sensors and cameras. In 2017, we identified the symbiotic relationship between edge computing and video analytics in an article, noting that live video analytics is the “killer app” for edge computing. Edge devices come in various shapes and sizes but are inherently resource-constrained relative to the cloud. 

These resource constraints necessitate lightweight machine learning (ML) models at the edge. Using techniques for model specialization and compression, the community has obtained edge models whose compute and memory footprints are substantially lower (by 96x for object detector models). Such models are super amenable to deploy at the edge. 

Smooth going so far, but the villain in the story is data drift! This is the phenomenon where the live data in the field diverges significantly from the initial training data. We achieved the phenomenally low compute footprints for edge models only because we specialized the models to be specific to the camera streams. But in the bargain, they lost their ability to generalize much beyond what they have seen during training. This lack of generality comes back to bite us when data drifts and accuracy of the models drop – by as much as 22% – when they are deployed in the field. 

Ekya is a solution, developed with collaborators at University of California, Berkeley and University of Chicago, that addresses the problem of data drift on the edge compute box. Instead of sending video data to the cloud for periodic retraining of models, which is costly in its bandwidth usage and can raise privacy questions, Ekya enables both retraining and inference to co-exist on the edge box. For more details, take a look at our paper: Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers, which has been published at NSDI 2022. We are excited to release the code for Ekya as well. 

Not only can you use the code to reproduce all experiments in our paper, we also hope that the code can help you easily build a continuous learning system for your edge deployment. Oh, and one more thing—we are also pointing to the raw video datasets released by the City of Bellevue. This includes 101 hours of video from five traffic intersections, all of which have also been labeled with our golden YOLOv3 model. We hope that the videos from the City of Bellevue as well as the other datasets included in the repository will aid in the building of new edge models as well as improving our pre-trained specialized models to significantly advance the state of the art.

Please reach out to Ganesh Ananthanarayanan with any questions.

The post Don’t let data drift derail edge compute machine learning models appeared first on Microsoft Research.

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