In today's fast-paced world, the Internet of Things (IoT) has become a ubiquitous presence, connecting everyday devices and providing real-time data insights. Within the IoT ecosystem, one of the most exciting developments is the integration of artificial intelligence (AI) and machine learning (ML) at the edge. This article explores the challenges and solutions in implementing machine learning models on resource-constrained IoT devices, with a focus on software engineering considerations for model optimization and deployment.
Introduction
The convergence of IoT and AI has opened up a realm of possibilities, from autonomous drones to smart home devices. However, IoT devices, often located at the edge of the network, typically have limited computational resources, making the deployment of resource-intensive machine learning models a significant challenge. Nevertheless, this challenge can be overcome through efficient software engineering practices.