Course Outline
Introduction to Edge AI and TinyML
- Overview of AI at the edge
- Benefits and challenges of running AI on devices
- Use cases in robotics and automation
Fundamentals of TinyML
- Machine learning for resource-constrained systems
- Model quantization, pruning, and compression
- Supported frameworks and hardware platforms
Model Development and Conversion
- Training lightweight models using TensorFlow or PyTorch
- Converting models to TensorFlow Lite and PyTorch Mobile
- Testing and validating model accuracy
On-Device Inference Implementation
- Deploying AI models to embedded boards (Arduino, Raspberry Pi, Jetson Nano)
- Integrating inference with robotic perception and control
- Running real-time predictions and monitoring performance
Optimization for Edge Performance
- Reducing latency and energy consumption
- Hardware acceleration using NPUs and GPUs
- Benchmarking and profiling embedded inference
Edge AI Frameworks and Tools
- Working with TensorFlow Lite and Edge Impulse
- Exploring PyTorch Mobile deployment options
- Debugging and tuning embedded ML workflows
Practical Integration and Case Studies
- Designing edge AI perception systems for robots
- Integrating TinyML with ROS-based robotics architectures
- Case studies: autonomous navigation, object detection, predictive maintenance
Summary and Next Steps
Requirements
- An understanding of embedded systems
- Experience with Python or C++ programming
- Familiarity with basic machine learning concepts
Audience
- Embedded developers
- Robotics engineers
- System integrators working on intelligent devices
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4350 € + VAT*
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Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
