TinyML with Wio Terminal

This course focuses on practical deployment of machine learning models on edge devices using Wio Terminal and TinyML. You will learn how to prepare data, train compact models, convert them into efficient formats, and deploy them on low-power microcontrollers for fast, offline decision-making.

The course is structured to help engineers, developers, and students create intelligent embedded systems without requiring cloud connectivity.

What is TinyML? TinyML refers to machine learning models that are optimized to run on low-power, small-footprint devices like MCUs. TinyML is cost-effective, allowing more individuals to train their models. Compatible with Arduino, Raspberry Pi, and other IoT devices, TinyML is the only platform that lets you know when you're making a mistake.

What is Wio Terminal? Wio Terminal is a device that makes it easy to interface with sensors and other hardware. It's a desktop application for quickly publishing your site without needing any knowledge of programming languages. You'll learn the basics of creating websites and interfacing with hardware.

Key Concepts Covered

• Introduction to TinyML and Wio Terminal hardware architecture

• Collecting and preprocessing data for embedded model training

• Model training using TensorFlow Lite

• Converting and quantizing models for microcontroller deployment

• Uploading models to the Wio Terminal and running inferences

• Optimizing performance for real-time response

• Implementing use cases such as gesture recognition, sound classification, or anomaly detection

What You’ll Build

• A fully working TinyML inference system on Wio Terminal

• A data collection pipeline tailored for embedded hardware

• A real-time sensor-based ML project (e.g., motion classification or sound response)

• Model loading and activation code in Arduino-compatible environment

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Target Audience

• Embedded developers entering machine learning

• Engineers interested in edge computing

• Students and researchers working on smart devices

• Makers with an interest in low-power AI

• Professionals seeking to implement ML without relying on the cloud

Prerequisites

• Basic Python programming

• Familiarity with Arduino IDE and embedded hardware

• Access to a Wio Terminal

• Installation of Arduino libraries and TensorFlow Lite environment (guided in the course)

Course Outcomes

• Create TinyML applications that run efficiently on Wio Terminal

• Understand the workflow from model training to deployment

• Implement lightweight models using TensorFlow Lite Micro

• Integrate ML inference into real-world sensing applications

• Work with onboard sensors like accelerometer, microphone, and display

What’s Included

• Step-by-step video tutorials

• Source code, libraries, and model files

• Downloadable datasets and templates

• Full documentation for reproducibility

• Certificate of Completion

Instructor Bio

The Educational Engineering Team, with over 250,000 enrolled learners, specializes in applied microcontroller training. Led by Ashraf, the team provides clear, practical instruction in embedded systems, automation, and applied AI. Their experience with Wio Terminal and TinyML allows them to deliver direct-to-device machine learning deployment strategies.

Start Deploying Machine Learning at the Edge

Use TinyML and Wio Terminal to develop efficient, real-time AI solutions that operate without cloud dependency. Build embedded intelligence using industry-standard tools.

Enroll Now – Apply TinyML on Wio Terminal