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Course Outline
Introduction to TinyML and IoT
- What is TinyML?
- Benefits of TinyML in IoT applications.
- Comparison of TinyML with traditional cloud-based AI.
- Overview of TinyML tools: TensorFlow Lite, Edge Impulse.
Setting Up the TinyML Environment
- Installing and configuring Arduino IDE.
- Setting up Edge Impulse for TinyML model development.
- Understanding microcontrollers for IoT (ESP32, Arduino, Raspberry Pi Pico).
- Connecting and testing hardware components.
Developing Machine Learning Models for IoT
- Collecting and preprocessing IoT sensor data.
- Building and training lightweight ML models.
- Converting models to TensorFlow Lite format.
- Optimizing models for memory and power constraints.
Deploying AI Models on IoT Devices
- Flashing and running ML models on microcontrollers.
- Validating model performance in real-world IoT scenarios.
- Debugging and optimizing TinyML deployments.
Implementing Predictive Maintenance with TinyML
- Using ML for equipment health monitoring.
- Sensor-based anomaly detection techniques.
- Deploying predictive maintenance models on IoT devices.
Smart Sensors and Edge AI in IoT
- Enhancing IoT applications with TinyML-powered sensors.
- Real-time event detection and classification.
- Use cases: environmental monitoring, smart agriculture, industrial IoT.
Security and Optimization in TinyML for IoT
- Data privacy and security in edge AI applications.
- Techniques for reducing power consumption.
- Future trends and advancements in TinyML for IoT.
Summary and Next Steps
Requirements
- Experience with IoT or embedded systems development.
- Familiarity with Python or C/C++ programming.
- Basic understanding of machine learning concepts.
- Knowledge of microcontroller hardware and peripherals.
Audience
- IoT developers.
- Embedded engineers.
- AI practitioners.
21 Hours
Testimonials (1)
The oral skills and human side of the trainer (Augustin).
