What is Edge AI Anomaly Detection?

Most of us can envision the transformative potential of Machine Learning and Edge AI in industrial applications. When it comes to vision-based ML solutions, the opportunities are virtually endless. However, bridging the gap from concept to implementation, especially when retrofitting legacy machinery, can seem insurmountable. We want to change that.

Edge AI is the use of artificial intelligence directly on connected devices, this is also referred to as 'on the edge'. This approach allows for real-time data processing and decision-making without the need for constant cloud connectivity.

Now, imagine walking through a busy factory floor, machines humming, sensors collecting data from every corner. This is where AI anomaly detection has become a game-changer for manufacturing. These smart systems are keeping a watchful eye on everything, learning what "normal" looks like so they can spot when something's off. That could be a motor running slightly hotter than usual or subtle variations in product dimensions - things humans might miss until they become serious problems. Anomaly Detection transforms maintenance from reactive to predictive, catching and solving issues before they cause problems and downtime.

As factories get more complex and data-heavy, these AI systems have become essential partners in keeping operations running smoothly, producing higher quality products, and keeping workers safe.

Build an Anomaly Detection Device

Catch up on the webinar to see firsthand how we built a robust "industrial vision" system that detects product anomalies with Edge AI, syncs relevant data in real-time via email and SMS using Blues' cellular connectivity, and are deployed using a Raspberry Pi 5 and Jetson Orin Nano Super.

Watch the recording to learn:

1. Building a vision-based anomaly detection model with Edge Impulse Studio, enabling machine learning model creation with minimal technical expertise.

2. Deploying the ML model to a Raspberry Pi 5, while demonstrating scalable pathways using a Pi Compute Module and a Jetson Orin Nano Super to meet the demands of industrial-scale deployments.

3. Using Blues Notecard to securely sync generated inferences and alerts via cellular connectivity, bypassing traditional IT network constraints.

As an added bonus, we'll show off a new Notecard Cellular option that provides the high-bandwidth throughput needed for vision-based applications - at an even lower cost.

This presentation is ideal for embedded and IoT engineers looking to leverage Machine Learning and wireless connectivity to build smarter, more responsive industrial systems.