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Vijay Janapa Reddi
and
Pete Warden
Have you wanted to build a TinyML device? In Deploying TinyML, you will learn the software, write the code, and deploy the model to your own tiny microcontroller-based device. Before you know it, you’ll be implementing an entire TinyML application.
Read more
Have you wanted to build a TinyML device? In Deploying TinyML, you will learn the software, write the code, and deploy the model to your own tiny microcontroller-based device. Before you know it, you’ll be implementing an entire TinyML application.
Read more
Have you wanted to build a TinyML device? In Deploying TinyML, you will learn the software, write the code, and deploy the model to your own tiny microcontroller-based device. Before you know it, you’ll be implementing an entire TinyML application.
A one-of-a-kind course, Deploying TinyML is a mix of computer science and electrical engineering. Gain hands-on experience with embedded systems, machine learning training, and machine learning deployment using TensorFlow Lite for Microcontrollers, to make your own microcontroller operational for implementing applications such as voice recognition, sound detection, and gesture detection.
The course features projects based on a TinyML Program Kit that includes an Arduino board with onboard sensors and an ARM Cortex-M4 microcontroller. The kit has everything you need to build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application. You can preorder your Arduino kit here.
Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The third course in the TinyML Professional Certificate program, Deploying TinyML provides hands-on experience with deploying TinyML to a physical device.
Register for this course and see more details by visiting:
OpenCourser.com/course/6ydunc/deploying
What's inside
Learning objectives
- An understanding of the hardware of a microcontroller-based device
- A review of the software behind a microcontroller-based device
- How to program your own tinyml device
- How to write your code for a microcontroller-based device
- How to deploy your code to a microcontroller-based device
- How to train a microcontroller-based device
- Responsible ai deployment
- An understanding of the hardware of a microcontroller-based device
- A review of the software behind a microcontroller-based device
- How to program your own tinyml device
- How to write your code for a microcontroller-based device
- How to deploy your code to a microcontroller-based device
- How to train a microcontroller-based device
- Responsible ai deployment
Syllabus
Introduction to the TinyML Kit
Deploying TinyML Applications on Embedded Devices
Collecting a Custom TinyML Dataset
Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
Read more
Syllabus
Introduction to the TinyML Kit
Deploying TinyML Applications on Embedded Devices
Collecting a Custom TinyML Dataset
Pre and Post Processing for Keyword Spotting, Visual Wake Words, and Gesturing a Magic Wand
Read more
Profiling and Optimization of TinyML Applications
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Strong focus on deploying TinyML to physical devices, making it highly relevant to practical applications
Involves hands-on experience with embedded systems, machine learning training, and deployment, providing valuable practical skills
Leverages TensorFlow Lite for Microcontrollers, a widely adopted framework for TinyML development
Utilizes an Arduino-based TinyML Program Kit, making it accessible to learners with limited hardware experience
Facilitated by instructors Vijay Janapa Reddi and Pete Warden, who are renowned for their contributions to TinyML
The emphasis on responsible AI deployment aligns with industry best practices and ethical considerations
Save this course
Save Deploying TinyML to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deploying TinyML with these
activities:
Complete a Udemy course on Embedded Systems
Show steps
Prepare for the course by refreshing your fundamentals in embedded systems. This will enhance your understanding of the hardware and software concepts used in TinyML devices.
Browse courses on
Embedded Systems
Show steps
-
Identify a beginners-level course on Embedded Systems on Udemy.
-
Complete the video lectures and read the course material.
-
Complete the assignments and quizzes to reinforce your learning.
Follow a tutorial on TensorFlow Lite for Microcontrollers
Show steps
Familiarize yourself with the toolkit used in the course by completing a guided tutorial on TensorFlow Lite for Microcontrollers.
Show steps
-
Find a comprehensive tutorial on TensorFlow Lite for Microcontrollers.
-
Set up your development environment following the tutorial.
-
Implement a simple machine learning model on a microcontroller using the tutorial's guidance.
Build a basic C++ program
Show steps
Refresh your C++ programming skills by building a simple program to strengthen your foundational understanding.
Browse courses on
C++ Programming
Show steps
-
Set up a C++ development environment
-
Create a new C++ project
-
Write a simple program that prints 'Hello, world!'
-
Compile and run the program
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Organize a study group with classmates
Show steps
Enhance your understanding by collaborating with peers in a study group.
Show steps
-
Connect with classmates and form a study group.
-
Establish regular meeting times and topics for discussion.
-
Take turns leading discussions and sharing perspectives.
Complete coding exercises on deploying TinyML models
Show steps
Solidify your understanding of TinyML deployment by practicing with coding exercises that challenge you to apply your knowledge in a hands-on environment.
Browse courses on
Machine Learning Deployment
Show steps
-
Find a platform or website that offers TinyML deployment coding exercises
-
Select exercises that align with your learning objectives
-
Attempt to solve the exercises independently
-
Review your solutions and identify areas for improvement
Solve TinyML coding challenges
Show steps
Develop your coding skills specific to TinyML by solving coding challenges.
Show steps
-
Find online coding challenges related to TinyML.
-
Attempt to solve the challenges using the concepts learned in the course.
-
Compare your solutions with others and discuss your approach in forums.
Follow a TensorFlow Lite for Microcontrollers tutorial
Show steps
Deepen your understanding of TensorFlow Lite for Microcontrollers by following an online tutorial that guides you through building a simple TinyML application.
Browse courses on
TensorFlow Lite
Show steps
-
Find a suitable TensorFlow Lite for Microcontrollers tutorial
-
Set up the necessary hardware and software
-
Follow the tutorial steps to build the TinyML application
-
Test and evaluate the application's performance
Write a blog post about your TinyML project
Show steps
Consolidate your knowledge and share your learnings by documenting your TinyML project in a blog post, providing a valuable resource for others interested in the field.
Browse courses on
Machine Learning Projects
Show steps
-
Choose an aspect of your TinyML project to focus on
-
Outline the key points you want to convey
-
Write the blog post, ensuring clarity and technical accuracy
-
Proofread and edit the post carefully
-
Publish the blog post on a relevant platform
Build a TinyML project based on the Arduino kit
Show steps
Apply your knowledge by building a practical TinyML project using the Arduino kit provided in the course.
Show steps
-
Identify a simple TinyML application, such as gesture recognition or sound detection.
-
Design and implement the hardware and software components of your project.
-
Test and evaluate your project's performance.
Develop a custom TinyML dataset
Show steps
Enhance your TinyML skills by creating a custom dataset tailored to a specific application, ensuring the availability of relevant and representative data for training your models.
Browse courses on
Data Collection
Show steps
-
Define the scope and purpose of the dataset
-
Identify and gather relevant data sources
-
Preprocess and clean the data
-
Split the data into training and testing sets
-
Evaluate the quality and representativeness of the dataset
Contribute to an open-source TinyML project
Show steps
Deepen your understanding and gain practical experience by contributing to an open-source TinyML project.
Browse courses on
Software Development
Show steps
-
Identify a suitable open-source TinyML project on platforms like GitHub.
-
Familiarize yourself with the project's codebase and documentation.
-
Identify a bug or feature to contribute to.
-
Implement and submit a pull request with your contribution.
Join a TinyML community and assist other learners
Show steps
Enhance your understanding by helping others in a TinyML community or forum.
Browse courses on
Peer Support
Show steps
-
Join a TinyML online community or forum.
-
Actively participate in discussions and provide support to other learners.
-
Share your knowledge and experiences to help others succeed.
Develop a TinyML application for a specific use case
Show steps
Apply your TinyML knowledge to a practical scenario by developing a fully functional TinyML application tailored to a specific use case, providing a tangible demonstration of your skills.
Show steps
-
Identify a specific use case and problem statement
-
Gather data and prepare a dataset
-
Design and develop the TinyML model
-
Deploy the model to a microcontroller-based device
-
Test and evaluate the application's performance
Complete a Udemy course on Embedded Systems
Show steps
Prepare for the course by refreshing your fundamentals in embedded systems. This will enhance your understanding of the hardware and software concepts used in TinyML devices.
Browse courses on
Embedded Systems
Show steps
Show steps
Show steps
- Identify a beginners-level course on Embedded Systems on Udemy.
- Complete the video lectures and read the course material.
- Complete the assignments and quizzes to reinforce your learning.
Follow a tutorial on TensorFlow Lite for Microcontrollers
Show steps
Familiarize yourself with the toolkit used in the course by completing a guided tutorial on TensorFlow Lite for Microcontrollers.
Show steps
Show steps
Show steps
- Find a comprehensive tutorial on TensorFlow Lite for Microcontrollers.
- Set up your development environment following the tutorial.
- Implement a simple machine learning model on a microcontroller using the tutorial's guidance.
Build a basic C++ program
Show steps
Refresh your C++ programming skills by building a simple program to strengthen your foundational understanding.
Browse courses on
C++ Programming
Show steps
Show steps
Show steps
- Set up a C++ development environment
- Create a new C++ project
- Write a simple program that prints 'Hello, world!'
- Compile and run the program
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Organize a study group with classmates
Show steps
Enhance your understanding by collaborating with peers in a study group.
Show steps
Show steps
Show steps
- Connect with classmates and form a study group.
- Establish regular meeting times and topics for discussion.
- Take turns leading discussions and sharing perspectives.
Complete coding exercises on deploying TinyML models
Show steps
Solidify your understanding of TinyML deployment by practicing with coding exercises that challenge you to apply your knowledge in a hands-on environment.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Find a platform or website that offers TinyML deployment coding exercises
- Select exercises that align with your learning objectives
- Attempt to solve the exercises independently
- Review your solutions and identify areas for improvement
Solve TinyML coding challenges
Show steps
Develop your coding skills specific to TinyML by solving coding challenges.
Show steps
Show steps
Show steps
- Find online coding challenges related to TinyML.
- Attempt to solve the challenges using the concepts learned in the course.
- Compare your solutions with others and discuss your approach in forums.
Follow a TensorFlow Lite for Microcontrollers tutorial
Show steps
Deepen your understanding of TensorFlow Lite for Microcontrollers by following an online tutorial that guides you through building a simple TinyML application.
Browse courses on
TensorFlow Lite
Show steps
Show steps
Show steps
- Find a suitable TensorFlow Lite for Microcontrollers tutorial
- Set up the necessary hardware and software
- Follow the tutorial steps to build the TinyML application
- Test and evaluate the application's performance
Write a blog post about your TinyML project
Show steps
Consolidate your knowledge and share your learnings by documenting your TinyML project in a blog post, providing a valuable resource for others interested in the field.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Choose an aspect of your TinyML project to focus on
- Outline the key points you want to convey
- Write the blog post, ensuring clarity and technical accuracy
- Proofread and edit the post carefully
- Publish the blog post on a relevant platform
Build a TinyML project based on the Arduino kit
Show steps
Apply your knowledge by building a practical TinyML project using the Arduino kit provided in the course.
Show steps
Show steps
Show steps
- Identify a simple TinyML application, such as gesture recognition or sound detection.
- Design and implement the hardware and software components of your project.
- Test and evaluate your project's performance.
Develop a custom TinyML dataset
Show steps
Enhance your TinyML skills by creating a custom dataset tailored to a specific application, ensuring the availability of relevant and representative data for training your models.
Browse courses on
Data Collection
Show steps
Show steps
Show steps
- Define the scope and purpose of the dataset
- Identify and gather relevant data sources
- Preprocess and clean the data
- Split the data into training and testing sets
- Evaluate the quality and representativeness of the dataset
Contribute to an open-source TinyML project
Show steps
Deepen your understanding and gain practical experience by contributing to an open-source TinyML project.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Identify a suitable open-source TinyML project on platforms like GitHub.
- Familiarize yourself with the project's codebase and documentation.
- Identify a bug or feature to contribute to.
- Implement and submit a pull request with your contribution.
Join a TinyML community and assist other learners
Show steps
Enhance your understanding by helping others in a TinyML community or forum.
Browse courses on
Peer Support
Show steps
Show steps
Show steps
- Join a TinyML online community or forum.
- Actively participate in discussions and provide support to other learners.
- Share your knowledge and experiences to help others succeed.
Develop a TinyML application for a specific use case
Show steps
Apply your TinyML knowledge to a practical scenario by developing a fully functional TinyML application tailored to a specific use case, providing a tangible demonstration of your skills.
Show steps
Show steps
Show steps
- Identify a specific use case and problem statement
- Gather data and prepare a dataset
- Design and develop the TinyML model
- Deploy the model to a microcontroller-based device
- Test and evaluate the application's performance
Career center
Learners who complete Deploying TinyML will develop knowledge and skills
that may be useful to these careers:
Embedded Software Engineer
Embedded Software Engineer
Embedded Software Engineers design, develop, test, and maintain firmware for embedded systems, which are computer systems designed to perform specific tasks within a larger electronic system. This course can help you build a foundation in embedded systems programming, which is essential for success as an Embedded Software Engineer. You will learn how to write code for microcontrollers, deploy your code to embedded devices, and train your devices to perform specific tasks.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and maintain robots. They work in a variety of industries, including manufacturing, healthcare, and space exploration. This course can help you build a foundation in embedded systems programming, which is essential for success as a Robotics Engineer. You will learn how to write code for microcontrollers, deploy your code to embedded devices, and train your devices to perform specific tasks. This knowledge can be applied to the development of robots that can perform a variety of tasks, such as manufacturing, healthcare, and space exploration.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They work in a variety of industries, including finance, healthcare, and retail. This course can help you build a foundation in machine learning, which is essential for success as a Data Scientist. You will learn how to train machine learning models, deploy your models to embedded devices, and use your models to solve business problems.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They work in a variety of industries, including finance, healthcare, and retail. This course can help you build a foundation in machine learning, which is essential for success as a Machine Learning Engineer. You will learn how to train machine learning models, deploy your models to embedded devices, and use your models to solve business problems.
Computer Scientist
Computer Scientist
Computer Scientists research, design, and develop computer systems. They work in a variety of industries, including software development, hardware design, and networking. This course can help you build a foundation in embedded systems programming, which is essential for success as a Computer Scientist. You will learn how to write code for microcontrollers, deploy your code to embedded devices, and train your devices to perform specific tasks.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems. They work in a variety of industries, including manufacturing, construction, and transportation. This course can help you build a foundation in embedded systems programming, which is essential for success as an Electrical Engineer. You will learn how to write code for microcontrollers, deploy your code to embedded devices, and train your devices to perform specific tasks.
Hardware Engineer
Hardware Engineer
Hardware Engineers design, develop, and maintain hardware systems. They work in a variety of industries, including manufacturing, construction, and transportation. This course may be useful for you if you are interested in designing embedded systems hardware. You will learn about the hardware of embedded systems and how to write code for microcontrollers.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and retail. This course may be useful for you if you are interested in developing embedded systems software. You will learn how to write code for microcontrollers and deploy your code to embedded devices.
Technical Writer
Technical Writer
Technical Writers create documentation for technical products. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in writing documentation for embedded systems products. You will learn about the hardware and software of embedded systems and how to write effective documentation.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in managing embedded systems projects. You will learn about the hardware and software of embedded systems and how to manage embedded systems projects.
Business Analyst
Business Analyst
Business Analysts analyze business needs and develop solutions to meet those needs. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in analyzing business needs for embedded systems products. You will learn about the hardware and software of embedded systems and how to analyze business needs for embedded systems products.
Product Manager
Product Manager
Product Managers research, design, and develop products. They work in a variety of industries, including consumer electronics, software, and hardware. This course may be useful for you if you are interested in managing embedded systems products. You will learn about the hardware and software of embedded systems and how to deploy embedded systems products to market.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing plans for products and services. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in marketing embedded systems products. You will learn about the hardware and software of embedded systems and how to market embedded systems products to customers.
Sales Engineer
Sales Engineer
Sales Engineers sell technical products and services. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in selling embedded systems products. You will learn about the hardware and software of embedded systems and how to sell embedded systems products to customers.
Quality Assurance Engineer
Quality Assurance Engineer
Quality Assurance Engineers test and evaluate software and hardware products to ensure that they meet quality standards. They work in a variety of industries, including software, hardware, and manufacturing. This course may be useful for you if you are interested in testing and evaluating embedded systems products. You will learn about the hardware and software of embedded systems and how to test and evaluate embedded systems products.
For more career information including salaries, visit:
OpenCourser.com/course/6ydunc/deploying
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deploying TinyML.
Save
Provides a comprehensive overview of TinyML, with a focus on practical applications. It great resource for anyone who wants to learn how to apply TinyML to real-world problems.
Covers the fundamentals of TinyML, including hardware, software, and model deployment. It valuable resource for those who want to learn more about the technical aspects of TinyML.
Save
Focuses on the application of machine learning to embedded systems. It covers a wide range of topics, including model selection, training, and deployment. It valuable resource for those who want to learn how to develop and deploy TinyML applications.
Provides a comprehensive overview of embedded systems, including hardware, software, and applications. It useful resource for those who want to gain a deeper understanding of the underlying technologies used in TinyML devices.
Save
Provides a comprehensive overview of embedded systems design. It covers a wide range of topics, including hardware, software, and applications. It valuable resource for those who want to learn how to design and develop embedded systems.
Provides a comprehensive overview of embedded system design. It covers a wide range of topics, including hardware, software, and applications. It valuable resource for those who want to learn how to design and develop embedded systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6ydunc/deploying
Share
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Effort
2 to 4 hours per week for 5 weeks
Level
Intermediate
Via
edX
Institution
Harvard University
Instructors
Vijay Janapa Reddi
Pete Warden
Session
April 24, 2024
- April 23, 2025
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Tiny Machine Learning (TinyML). It's comprised of three courses:
- Applications of TinyML
- Deploying TinyML (viewing)
- Fundamentals of TinyML
Good to know
Strong focus on deploying TinyML to physical devices, making it highly relevant to practical applications
Involves hands-on experience with embedded systems, machine learning training, and deployment, providing valuable practical skills
Leverages TensorFlow Lite for Microcontrollers, a widely adopted framework for TinyML development
Utilizes an Arduino-based TinyML Program Kit, making it accessible to learners with limited hardware experience
Facilitated by instructors Vijay Janapa Reddi and Pete Warden, who are renowned for their contributions to TinyML
The emphasis on responsible AI deployment aligns with industry best practices and ethical considerations
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