AI Training for Embedded Systems
Expert-led courses for embedded engineers and their managers
Browse all 25 courses on do.instituteAI-Assisted Coding
AI tools for embedded C/C++ development - Claude Code, GitHub Copilot, prompt engineering for firmware
and 17 more courses in this category
Explore AI-Assisted CodingAI Product Strategy
Strategic frameworks for managers - AI opportunity assessment, feasibility analysis, team capability building, change management
and 2 more courses in this category
Explore AI Product StrategySafety-Critical AI
Safety-critical AI systems - ISO 26262, IEC 61508, adversarial robustness, explainability in safety-critical contexts
and 3 more courses in this category
Explore Safety-Critical AIFurther categories you might be interested in:Edge ML Deployment · AI for Requirements & Product · AI for Testing & QA · AI for Architecture & Design
Two Ways to Master AI for Embedded Systems
Most teams use AI as glorified code completion and get 10% productivity gains. We teach deep integration for 10x gains. Master AI in two ways - deploy machine learning on resource-constrained devices (TinyML, edge inference) AND transform your development workflow (AI-assisted coding, testing, requirements). Our expert practitioners deliver embedded-native AI training that respects hardware constraints, safety requirements, and your need for reliability. Whether you’re an engineer adding AI to products or a manager navigating team transformation, we provide practical guidance grounded in embedded realities.
AI-Enabled Projects
Edge ML Deployments
AI-Assisted Workflows
Trusted by Embedded Engineers







