MLOps for Scaling TinyML
Experience MLOps through TinyML.
This online course introduces learners to Machine Learning Operations (MLOps) through the lens of TinyML (Tiny Machine Learning).
2-4 hours per week
What You'll Learn
Are you ready to scale your (tiny) machine learning application? Do you have the infrastructure in place to grow? Do you know what resources you need to take your product from a proof-of-concept algorithm on a device to a substantial business?
Machine Learning (ML) is more than just technology and an algorithm; it's about deployment, consistent feedback, and optimization. Today, more than 87% of data science projects never make it into production. To support organizations in coming up to speed faster in this critical domain it is essential to understand Machine Learning Operations (MLOps). This course introduces you to MLOps through the lens of TinyML (Tiny Machine Learning) to help you deploy and monitor your applications responsibly at scale.
MLOps is a systematic way of approaching Machine Learning from a business perspective. This course will teach you to consider the operational concerns around Machine Learning deployment, such as automating the deployment and maintenance of a (tiny) Machine Learning application at scale. In addition, you’ll learn about relevant advanced concepts including neural architecture search, allowing you to optimize your models' architectures automatically; federated learning, allowing your devices to learn from each other; and benchmarking, enabling you to performance test your hardware before pushing the models into production.
This course focuses on MLOps for TinyML (Tiny Machine Learning) systems, revealing the unique challenges for TinyML deployments. Through real-world examples, you will learn how tiny devices, such as Google Homes or smartphones, are deployed and updated once they’re with the end consumer, experiencing the complete product life cycle instead of just laboratory examples.
Are you ready for a billion users?
The course will be delivered via edX and connect learners around the world. By the end of the course, participants will learn:
- Know why and when deploying MLOps can help your (tiny) product or business
- Key MLOps platform features that you can deploy for your data science project
- How to automate a MLOps life cycle
- Real-world examples and case studies of MLOps Platforms targeting tiny device
Your Instructors
Vijay Janapa Reddi
Associate Professor at John A. Paulson,
School of Engineering and Applied Sciences (SEAS),
at Harvard University
Read full bio.
Ways to take this course
When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.
A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate.
Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live