AI Training for Embedded Systems
Expert-led courses for embedded engineers and their managers
AI-Assisted Coding
AI tools for embedded C/C++ development - Claude Code, GitHub Copilot, prompt engineering for firmware
and 16 more courses in this category
Browse AI-Assisted Coding courses on do.instituteEdge ML Deployment
Deploy machine learning on microcontrollers - TinyML, model optimization, real-time inference on resource-constrained hardware
and 1 more courses in this category
Browse Edge ML Deployment courses on do.instituteAI for Requirements & Product
AI in requirements engineering and product discovery - AI-enhanced specifications, product ideation workshops
and 0 more courses in this category
Browse AI for Requirements & Product courses on do.instituteAI Product Strategy
Strategic frameworks for managers - AI opportunity assessment, feasibility analysis, team capability building, change management
and 2 more courses in this category
Browse AI Product Strategy courses on do.instituteSafety-Critical AI
Safety-critical AI systems - ISO 26262, IEC 61508, adversarial robustness, explainability in safety-critical contexts
and 3 more courses in this category
Browse Safety-Critical AI courses on do.instituteTwo 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







