Saradindu Sengupta
Specialises in computer vision, time-series forecasting, and scalable MLOps frameworks with over five years of experience in ML engineering.
Develops and deploys production-grade ML solutions in automotive, telematics, and clean-tech domains.
Leads real-time inferencing platform development and ADAS calibration model deployment at Lytx, scaling across 100,000+ devices.
Designed predictive maintenance and anomaly detection models at Nunam Technologies, building MLOps pipelines with Kubeflow, KServe, and MLflow on AWS.
Contributes to open-source projects such as MLPerf-Tiny and delivers technical talks at major ML conferences.
Session
Deploying real-time AI models on embedded Linux platforms like Raspberry Pi, Jetson Nano, or Rockchip-based boards is a growing need in industries like manufacturing, healthcare, and automotive. However, the challenges are real: constrained computing, tight memory, and limited power. This hands-on workshop walks you through the full lifecycle—designing, optimising, cross-compiling, and deploying lightweight CNNS for inference at the edge.
Participants will start with a base CNN (e.g., MobileNet or ShuffleNet), apply model compression techniques like pruning and quantisation, and then learn how to build optimised deployment pipelines using TensorFlow Lite and PyTorch Mobile. We'll also touch upon using NPU accelerators and real-time profiling to hit performance targets. By the end, the audience would be able to deploy and benchmark a real model on an embedded Linux system.