Python for ML and AI
2-3 weeksSkills You'll Build
Python programming fundamentalsNumPy and data manipulationML/AI Python ecosystemBridging C/C++ and PythonJupyter notebooks for experimentation
Embedded Engineer → Edge AI Engineer
Embedded Engineer to AI Engineer: From Hardware to Edge AI
Your embedded systems expertise is perfect for edge AI and on-device inference. This path leverages your understanding of resource constraints, optimization, and hardware to specialize in deploying AI models where they matter most, at the edge. Build AI systems that run on everything from microcontrollers to mobile devices.
4-6 months
Prerequisites
- Experience in embedded systems development
- Proficiency in C/C++ programming
- Understanding of hardware constraints and optimization
- Familiarity with microcontrollers and embedded Linux
- Experience with resource-constrained environments
Your Learning Path
ML Fundamentals and Model Architecture
3-4 weeksSkills You'll Build
Machine learning core conceptsNeural network architecturesModel complexity and size tradeoffsUnderstanding inference vs trainingModel selection for edge deployment
Edge AI and Model Optimization
4-5 weeksSkills You'll Build
Model quantization techniquesPruning and compressionTensorFlow Lite and ONNX RuntimeEdge inference frameworksBenchmarking and profiling
On-Device AI Implementation
4-5 weeksSkills You'll Build
Deploying models on embedded hardwareHardware acceleration (GPU, NPU, TPU)Memory management for AIPower optimization for AI workloadsReal-time inference requirements
Small Language Models and LLM Integration
3-4 weeksSkills You'll Build
Small language models for edgeLocal LLM deployment (Ollama, llama.cpp)Hybrid cloud-edge architecturesLLM API integration when cloud is neededBalancing local vs cloud inference
Portfolio and Career Transition
2-3 weeksSkills You'll Build
Building edge AI portfolio projectsShowcasing optimization expertiseEdge AI engineer interviewsNetworking in edge AI communityPositioning for specialized roles