Machine Learning Operations on ZYNQ FPGA Board for Real-Time Face Recognition

Abstract

Nowadays, MLOps approaches are gaining popularity thanks to their ability to apply DevOps best practices to machine learning models. They enable the automation and optimization of model training, deployment, and monitoring in various environments, while ensuring effective Continuous Integration/Continuous Deployment (CI/CD). These approaches thus promote real-time applications that can react quickly and improve continuously. This paper examines the feasibility of implementing MLOps practices in embedded systems, specifically on the Zynq-7000 FPGA board. We present a comprehensive MLOps architecture that enables the automated deployment and monitoring of a convolutional neural network model for face recognition on an embedded hardware platform for datacenter physical access control scenarios. This architecture integrates GitLab CI/CD for version control and pipeline automation, MLflow for experiment tracking and model lifecycles management, Prometheus and Grafana for monitoring, and data storage in an S3 Bucket cloud connected to DVC for dataset versioning. The results demonstrate that the proposed pipeline can be effectively deployed on a Zynq-7000 FPGA board enabling automated model retraining, redeployment, and performance monitoring. This approach reduces operational complexity and supports faster adaptation to dataset changes.