Deploying AI on Microcontrollers with TinyML Training Course

TinyML enables AI models to run efficiently on microcontrollers and edge devices with low power consumption.

This instructor-led, live training (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.

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 Edge AI

What is TinyML?
Advantages and challenges of AI on microcontrollers
Overview of TinyML tools: TensorFlow Lite and Edge Impulse
Use cases of TinyML in IoT and real-world applications

Setting Up the TinyML Development Environment

Installing and configuring Arduino IDE
Introduction to TensorFlow Lite for microcontrollers
Using Edge Impulse Studio for TinyML development
Connecting and testing microcontrollers for AI applications

Building and Training Machine Learning Models

Understanding the TinyML workflow
Collecting and preprocessing sensor data
Training machine learning models for embedded AI
Optimizing models for low-power and real-time processing

Deploying AI Models on Microcontrollers

Converting AI models to TensorFlow Lite format
Flashing and running models on microcontrollers
Validating and debugging TinyML implementations

Optimizing TinyML for Performance and Efficiency

Techniques for model quantization and compression
Power management strategies for edge AI
Memory and computation constraints in embedded AI

Practical Applications of TinyML

Gesture recognition using accelerometer data
Audio classification and keyword spotting
Anomaly detection for predictive maintenance

Security and Future Trends in TinyML

Ensuring data privacy and security in TinyML applications
Challenges of federated learning on microcontrollers
Emerging research and advancements in TinyML

Summary and Next Steps

Requirements

Experience with embedded systems programming
Familiarity with Python or C/C++ programming
Basic knowledge of machine learning concepts
Understanding of microcontroller hardware and peripherals

Audience

Embedded systems engineers
AI developers

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 £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.