Deploying TinyML

Course Description

Deploying TinyML is an innovative and hands-on course that bridges the gap between computer science and electrical engineering, offering students a unique opportunity to dive into the world of Tiny Machine Learning (TinyML). This intermediate-level course, part of the TinyML Professional Certificate program from HarvardX, focuses on the practical aspects of deploying machine learning models on microcontroller-based devices.

Throughout this course, you'll gain invaluable experience in embedded systems, machine learning training, and deployment using TensorFlow Lite for Microcontrollers. You'll work with a TinyML Program Kit, which includes an Arduino board equipped with onboard sensors and an ARM Cortex-M4 microcontroller, allowing you to build exciting applications in image recognition, audio processing, and gesture detection.

What students will learn

• Understanding of microcontroller-based device hardware
• Software principles behind microcontroller-based devices
• Programming and coding for TinyML devices
• Deploying machine learning models to microcontroller-based devices
• Training microcontroller-based devices
• Responsible AI deployment practices
• Collecting custom TinyML datasets
• Pre and post-processing techniques for various applications
• Profiling and optimization of TinyML applications

Prerequisites

• Completion of the "Applications of TinyML" course
• Basic programming knowledge in C/C++
• Access to a TinyML Course Kit (Arduino board with sensors)

Course Coverage

• Introduction to the TinyML Kit and its components
• Deploying TinyML applications on embedded devices
• Collecting and preparing custom TinyML datasets
• Pre and post-processing techniques for keyword spotting, visual wake words, and gesture recognition
• Profiling and optimization strategies for TinyML applications
• Hands-on projects using the Arduino board and TensorFlow Lite for Microcontrollers
• Responsible AI deployment considerations

Who this course is for

This course is ideal for intermediate-level learners with a background in computer science or electrical engineering who want to explore the exciting field of TinyML. It's perfect for developers, engineers, and hobbyists interested in implementing machine learning on resource-constrained devices and creating innovative IoT applications.

Real-world Applications

The skills acquired in this course have numerous real-world applications across various industries. Learners can apply their knowledge to develop:

• Smart home devices with voice recognition capabilities
• Wearable technology for health monitoring and activity tracking
• Industrial IoT sensors for predictive maintenance
• Environmental monitoring systems
• Security and surveillance solutions using computer vision
• Gesture-controlled devices for accessibility applications
• Energy-efficient smart city infrastructure
• Agricultural monitoring and automation systems

These skills are highly valuable in the rapidly growing fields of IoT, edge computing, and embedded AI, opening up numerous career opportunities and possibilities for innovation.

Syllabus

1. Introduction to the TinyML Kit
2. Deploying TinyML Applications on Embedded Devices
3. Collecting a Custom TinyML Dataset
4. Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
5. Profiling and Optimization of TinyML Applications