We Started with a Question
Back in 2019, we kept running into the same wall. Companies wanted IoT solutions, but most programs taught theory without showing how sensors actually talk to each other in a warehouse at 3 AM when something breaks.
From Frustration to Focus
The idea came from actual project work. We'd spent months helping a manufacturing client fix connectivity issues between their legacy systems and new IoT sensors. The problem wasn't complicated once you understood how protocols interact under network stress.
But finding engineers who knew this stuff? That was hard. Most had never worked with edge computing in conditions where milliseconds matter and connections drop randomly.
So we built our first course around real scenarios we'd encountered. Students worked with the same constraints we face on client projects—limited bandwidth, legacy hardware, and the need to process data locally before sending it anywhere.
That initial group of twelve students graduated in autumn 2020. By early 2021, three of them were working on IoT implementations at regional facilities. They weren't just following tutorials. They could diagnose why a sensor network was dropping packets at specific times or why an AI model wasn't making useful predictions from manufacturing data.
How We Actually Teach This
Most programs give you clean datasets and perfect network conditions. We don't, because your future projects won't either. Here's what makes our approach different from typical certification courses.
Real Equipment Problems
You'll work with sensor arrays that occasionally fail, network conditions that change unpredictably, and AI models that need optimization for devices with limited processing power. We simulate actual deployment environments where things go wrong at inconvenient times.
Messy Data Handling
Industrial sensors produce noisy data with gaps and outliers. Our students learn preprocessing techniques that work when your dataset isn't perfectly formatted and your time series has missing values because a sensor lost power for three hours.
Project-Based Learning
Instead of isolated exercises, you'll build complete systems from sensor selection through deployment. Recent projects included predictive maintenance systems for HVAC equipment and inventory tracking using computer vision—both based on real client requirements we've encountered.
Who's Teaching
Our instructors spend half their time working on active IoT deployments. When someone shows you how to configure a LoRaWAN gateway, they've probably debugged one in a warehouse last month. The curriculum changes based on what we're actually seeing in current projects.
Roderick Voss
Technical Director
Teaching Philosophy
Roderick believes the best way to learn IoT and AI integration is through troubleshooting. Each module includes scenarios where something isn't working correctly—just like in actual deployments. Students learn to diagnose issues systematically rather than just following installation guides. His background includes eight years working with industrial automation systems before focusing on education full-time in 2023.