Developing High-Speed Edge Sensor Packages for MLOps Reliability
High-speed inspection systems and autonomous platforms require clean, fast, and accurate input data. The challenge is that data captured at the edge is inherently noisy, inconsistent, and often too large (high-resolution images/logs) to transmit economically. Transmitting raw, unfiltered data compromised cloud storage costs and undermined model accuracy.
I specialize in architecting the "last mile" of the data pipeline: custom, networked edge sensor packages that perform intelligent pre-processing. This ensured the larger cloud-based inspection system received only clean, consistent, and compressed data, dramatically cutting costs and improving model stability.
Embedded AI Deployment (Jetson): Developed and deployed a neural network model directly onto a resource-constrained Jetson platform to perform real-time filtering and feature extraction at the point of capture, eliminating the need to send unnecessary raw data to the cloud.
Custom Sensor Integration (Arduino): Designed a specialized, cost-effective custom sensor package using an Arduino environment to acquire highly specific, clean data inputs, validating the ability to integrate non-standard or bespoke industrial hardware.
Networked Reliability: Ensured consistent communication protocols and reliable data transmission logic (e.g., error handling, compression) for remote data streams, protecting the system from connectivity failures.