Deploying TinyML on Microcontrollers
This course is part of multiple programs. Learn more.
Master the practical aspects of deploying machine learning models on microcontrollers. Through hands-on projects using Arduino and TensorFlow Lite, learn to build applications for voice recognition, gesture detection, and image processing on embedded systems.
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What you'll learn
Understand microcontroller hardware architecture and capabilities
Master programming for TinyML devices using TensorFlow Lite
Develop skills in custom dataset collection and preprocessing
Implement machine learning models on embedded devices
Optimize TinyML applications for performance and efficiency
Apply responsible AI deployment practices
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This practical course combines computer science and electrical engineering to teach deployment of machine learning models on microcontrollers. Students work with Arduino boards featuring ARM Cortex-M4 microcontrollers and onboard sensors to build real-world applications. The curriculum covers hardware understanding, software programming, data collection, model training, and optimization for embedded systems. Through hands-on projects, students learn to implement applications like voice recognition, sound detection, and gesture recognition using TensorFlow Lite for Microcontrollers.
Introduction to the TinyML Kit
Module 1
Deploying TinyML Applications on Embedded Devices
Module 2
Collecting a Custom TinyML Dataset
Module 3
Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
Module 4
Profiling and Optimization of TinyML Applications
Module 5
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Tiny Machine Learning (TinyML), Applied Tiny Machine Learning (TinyML) for Scale
Instructors
10 Courses
A Pioneer in Computer Architecture and Machine Learning Systems
Dr. Vijay Janapa Reddi serves as the John L. Loeb Associate Professor of Engineering and Applied Sciences at Harvard University and Vice President of MLCommons, where he drives innovation in machine learning as both co-founder and research chair. His research integrates computer architecture and machine learning systems to advance intelligence and autonomy in mobile devices, edge computing platforms, and IoT devices. After completing his BS in Computer Engineering from Santa Clara University, MS from the University of Colorado Boulder, and PhD in Computer Science from Harvard University, he established himself at the University of Texas at Austin before joining Harvard in 2019. His significant contributions include co-leading the development of MLPerf benchmarks, creating the Tiny Machine Learning series on edX reaching thousands of global learners, and pioneering work in mobile and edge computing systems. His exceptional achievements have earned him numerous accolades, including the NAE Gilbreth Lecturer Honor, IEEE TCCA Young Computer Architect Award, Intel Early Career Award, multiple Google Faculty Research Awards, and induction into both the MICRO and HPCA Halls of Fame. Beyond academia, he serves on the boards of MLCommons and the tinyML Foundation, while actively working to democratize machine learning education through initiatives like the Austin Independent School District's hands-on computer science program and the development of open-source educational resources.
1 Course
A Pioneering Leader in Mobile Machine Learning and Embedded AI
Pete Warden currently serves as CEO of Useful Sensors Inc., following his influential tenure as Staff Research Engineer and Technical Lead of TensorFlow Mobile at Google from 2014 to 2022. His journey began with creating innovative video processing filters that caught Apple's attention in 2003, leading to his work on Apple's imaging products. As founder and CTO of Jetpac, he developed groundbreaking photo analysis technology that processed over 140 million Instagram images before Google's acquisition in 2014. At Google, he spearheaded the development of TensorFlow Lite and TensorFlow Micro, revolutionizing machine learning on mobile and embedded devices. His contributions include creating frameworks that can run on devices with less than 100KB of memory and developing speech recognition models under 20KB. Beyond his technical achievements, he has authored multiple O'Reilly books including "Public Data Sources," "Big Data Glossary," and "TinyML," while maintaining an influential blog and speaking presence in the embedded AI community. Currently pursuing a PhD at Stanford University, he continues to innovate in the field of embedded machine learning through his work at Useful Sensors, focusing on bringing privacy-preserving ML capabilities to everyday devices.
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