tldr - powered by Generative AI

• Bringing machine intelligence closer to the edge offers significant advantages for IIoT and IoMT solutions
• The computational complexity of running machine learning on embedded or resource-constrained devices is a big challenge
• Optimization strategies, such as using Kubernetes to control and manage AIoT machine learning pipelines on the edge devices, can help solve these challenges
• The reference architecture for running AIoT ML Ops on the edge tier has four hardware tiers: training, platform, inference, and IoT
• The platform tier hosts various services, including a private container registry and an OTA ML code repo
• The inference and IoT tier uses MQTT and Kafka-based services for protocol bridging and communication
• A live demo simulating an industrial IoT setting shows how ML pipelines measure drift and re-train and re-deploy the model