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Course Outline
Introduction to TinyML and IoT
- Defining TinyML
- Advantages of TinyML in IoT contexts
- Comparing TinyML with traditional cloud-based AI
- Overview of key TinyML tools: TensorFlow Lite, Edge Impulse
Establishing the TinyML Environment
- Installing and configuring Arduino IDE
- Setting up Edge Impulse for TinyML model creation
- Exploring microcontrollers for IoT (ESP32, Arduino, Raspberry Pi Pico)
- Connecting and validating hardware components
Creating Machine Learning Models for IoT
- Gathering and preprocessing IoT sensor data
- Developing and training lightweight ML models
- Transforming models into TensorFlow Lite format
- Optimizing models for memory and power limitations
Deploying AI Models on IoT Devices
- Flashing and executing ML models on microcontrollers
- Assessing model performance in real-world IoT settings
- Troubleshooting and enhancing TinyML deployments
Applying Predictive Maintenance with TinyML
- Leveraging ML for equipment health surveillance
- Sensor-driven anomaly detection methods
- Implementing predictive maintenance models on IoT devices
Smart Sensors and Edge AI in IoT
- Augmenting IoT applications with TinyML-enabled sensors
- Real-time event identification and categorization
- Practical use cases: environmental monitoring, smart agriculture, industrial IoT
Security and Optimization in TinyML for IoT
- Data privacy and security considerations in edge AI
- Strategies for minimizing power consumption
- Emerging trends and future advancements in TinyML for IoT
Wrap-up and Subsequent Actions
Requirements
- Proficiency in IoT or embedded systems development
- Working knowledge of Python or C/C++ programming
- Fundamental understanding of machine learning principles
- Insight into microcontroller hardware and peripherals
Target Audience
- IoT Developers
- Embedded Engineers
- AI Professionals
21 Hours
