TinyML for IoT Applications

• 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

TinyML extends machine learning capabilities to ultra-low-power IoT devices, enabling real-time intelligence at the edge.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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