MLOps for TinyML: Scaling Machine Learning Systems
Learn to deploy, monitor, and scale tiny machine learning models in production through MLOps best practices.
Learn to deploy, monitor, and scale tiny machine learning models in production through MLOps best practices.
Master the operational aspects of deploying tiny machine learning systems at scale with this advanced course from Harvard. Learn how to bridge the gap between proof-of-concept algorithms and production-ready applications. Explore MLOps principles specifically tailored for TinyML deployments, including automated deployment pipelines, monitoring systems, and maintenance strategies. Perfect for professionals looking to scale their ML applications from laboratory experiments to real-world implementations.
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What you'll learn
Master MLOps principles for scaling TinyML applications
Implement automated deployment and monitoring systems
Optimize model architectures using neural architecture search
Deploy federated learning for distributed device networks
Establish effective benchmarking practices
Manage complete MLOps lifecycle automation
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This comprehensive course bridges the gap between machine learning development and production deployment in the context of TinyML systems. Students learn essential MLOps practices for successfully scaling ML applications, with a focus on the unique challenges of deploying to tiny devices. The curriculum covers key topics including automated deployment pipelines, monitoring systems, neural architecture search, federated learning, and performance benchmarking. Through real-world case studies and examples, students learn how to manage the complete product lifecycle of TinyML applications, from initial deployment to maintenance and updates.
Fee Structure
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.
4 Courses
A Pioneering Leader in AI, Data Science and Interplanetary Computing
Dr. Larissa Suzuki currently serves as Technical Director in Google's Office of the CTO, where she brings technical expertise in artificial intelligence advancement while also working as a Distinguished Visiting Researcher at NASA's Jet Propulsion Laboratory developing the Interplanetary Internet with Vint Cerf. Her remarkable journey began in Brazil, where at age 17 she initiated an educational program teaching mathematics and computing to over 13,000 economically disadvantaged students annually. After earning her PhD in Computer Science from UCL focusing on Data Infrastructures for Smart Cities, she founded multiple influential organizations including the UCL Society of Women Engineers and co-founded the London branch of the Anita Borg Institute. Her career spans leadership roles at Oracle, Arup Group, and the Mayor of London's office, where she created the City Data Market Strategy implemented across 40 European cities. A champion for diversity and inclusion, she draws from her personal experience with autism to advocate for neurodiversity in technology, speaking to audiences of up to 60,000 people. Her exceptional contributions have earned her numerous accolades including the Royal Academy of Engineering Rooke Award, Engineer of the Year Award, and recognition as one of the Top 50 Women in European Tech. Beyond her technical work, she serves as an Honorary Associate Professor at UCL, lecturer at Harvard and Oxford, and maintains roles as a pianist and violinist while continuing to advance initiatives in AI ethics, smart cities, and sustainable computing.
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