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What is TinyML?
With the growth of microcontrollers—tiny chips designed to perform a single function on a device—embedded devices are getting faster, more efficient, and less expensive. Machine learning works directly with the microcontroller to perform calculations and gather data is now a massive part of tech investment as manufacturers seek to expand what machine learning can do on these low power devices.
Tiny machine learning can run the algorithms necessary to perform deep learning in some cases, opening up the possibility of remote connection even in the harshest or barest of environments.
These tiny devices can run the machine learning models, thanks to a collaborative effort from machine learning communities and the embedded devices themselves. This deep learning could help with optimization in all kinds of tasks from storing satellite imagery to data collection for agriculture—many disparate systems may transform as embedded devices get smarter.
TinyML applications are an aspect of artificial intelligence, a growing field that's transforming nearly everything in human life. EdX.org offers courses in artificial intelligence, options created and taught by leading institutions and thinkers. You'll be able to connect with students around the world and take part in courses from beginner to advanced computation.
edX facilitates your exploration into the world of TinyML and many other fields through free courses that spark your interest and paid options that verify completion that you can send to employers or research institutions. It's time to take that next step.
TinyML Courses and Certifications
Harvard University offers a certification course in TinyML that will teach you the basic principles as well as what it takes to deploy in the field. During the first course, you'll study fundamentals, including the power it takes to optimize embedded devices, the deep learning models currently in use, and the compute power required to run TinyML models.
The second course is all about deploying these edge devices across use cases. Low-power microcontrollers provide a low-cost way to manage just about any device, and IoT applications could bring connection even in the most remote settings.
Finally, you'll put it all together to understand how ML models and TinyML projects look in the real world. You'll build skills in data analytics applications specifically for TinyML and understand how this automation will bring innovations and connections across industries.
The Promise of TinyML
Whether it's open-source programs built in Python and bringing connections to remote places or low-latency IoT devices for broad industry use, TinyML may help us reinforce our infrastructure and improve on our already connected ecosystem. edX and partners can help you build the skills you need to work with these edge compute devices in ways that breathe new life into anything with a microcontroller. It's time to take the next step with edX.