Data Systems & ML Ops Redesign

J Squared builds and provides rugged electronic systems in several industries. FALC-AI, a division within J Squared, is focused on the vertical integration of these platforms with computer vision and AI models to enable a variety of use cases that require being
able to extract accurate data from videos and images, and in turn, use that data to enable industry specific features. As a result of this, data from edge devices is streamed into the cloud for processing. With a low number of edge devices, the performance and storage requirements are pretty minimal. But, as more devices are added, the current solution will need improvements on the data processing side to be able to operate on the data in a performant and timely manner. This project will focus on researching, testing, and implementing a variety of improvements across the full data pipeline—from infrastructure setup and query optimization to data cleaning, library evaluations, and benchmarking—to ensure robust and scalable solutions for processing data from edge devices. The primary focus is optimizing the performance of a compute-intensive stage in the pipeline, where a YOLO-based object detection model and a Person Re-Identification (RE-ID) model, OSNet, run in parallel. Since the RE-ID model is particularly resource-intensive, it’s crucial that all data processing in this step is highly efficient to maximize overall system throughput.

Faculty Supervisor:

Shurui Zhou

Student:

Partner:

J-Squared Technologies

Discipline:

Computer science

Sector:

Manufacturing

University:

University of Toronto

Program: