The Embedded Systems school exists because the standard agile playbook breaks down where hardware enters the picture. “Fail fast” is fine for a web app; it’s a non-starter for a brake controller. Standups are useful, but they don’t fix the fact that your hardware engineer can only get bench time twice a week. The school’s job is to teach agile, DevOps, and engineering leadership practices that survive these constraints, instead of pretending the constraints aren’t there.
The largest cluster of courses is embedded-agile: agile, Scrum, SAFe, and product-management practices rewritten for hardware-software teams. We don’t translate cloud-native agile into embedded by hand-waving – we work through the specific frictions (hardware procurement cycles, multi-disciplinary teams, regulated domains) and the patterns that handle them. This includes the DASA DevOps and SAFe certification tracks adapted for embedded contexts.
The engineering practices track is the daily-tool layer: test-driven development for embedded, architecture for resource-constrained systems, embedded testing fundamentals, RTOS introduction. It also houses the AI-augmented development workshop for embedded firmware – the bridge to the Embedded AI school for teams whose engineers want to use modern coding assistants on driver-level work.
The DevOps and version control track addresses the part that gets hardest in embedded: continuous integration when hardware is in the loop, observability for embedded systems in production, and version control practices that work across hardware design files and firmware code.
Pick a single school course for depth on one specific topic. Pick the AI-Powered Embedded Development learning path if you want a five-day spaced-delivery programme on AI for embedded engineers, sequenced for you. The filter view below narrows the catalogue by your audience, level, and delivery language.