TinyML for IoT Applications Training Course

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.

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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

Delivery Options

Private Group Training

Our identity is rooted in delivering exactly what our clients need.

Pre-course call with your trainer
Customisation of the learning experience to achieve your goals -

Bespoke outlines
Practical hands-on exercises containing data / scenarios recognisable to the learners

Training scheduled on a date of your choice
Delivered online, onsite/classroom or hybrid by experts sharing real world experience

Private Group Prices RRP from €6840 online delivery, based on a group of 2 delegates, €2160 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.