Google and Harvard join forces to offer tiny machine learning course on EdX
Google’s open-source machine learning platform TensorFlow has teamed up with HarvardX to offer a certification programme on EdX that will train tech professionals in tiny machine learning (TinyML).
The tiny machine learning course will be taught by engineers from the TensorFlow group and Harvard professors, and can be completed in a few months.
Students in this course will learn about the emerging field of TinyML, its real-world applications, and the future possibilities of the transformative technology.
According to InformationWeek, “The program is meant to support this specialized segment of development that can include edge computing with smart devices, wildlife tracking, and other sensors. The program comprises a series of courses that can be completed at home.”
Anant Agarwal, CEO of EdX, explained that TinyML involves scaling machine learning to function in small form, edge devices that take up less power than desktop computers and have limited storage and processing capacity. These include devices that operate on batteries, such as remote sensors, microphones, and cameras set up in the wilderness.
He also said that TinyML is useful in cases where machine learning capabilities are limited by a lack of access to robust networks. Machine learning can work well on smartphones and tablets because of their ability to connect with computers and utilise cloud storage but is limited on small devices without that functionality.
For example, TinyML will come in handy when a motion sensor attached to a camera in the wilderness can be triggered to record wildlife passing by.
“When the device is small, it has to consume very low power and doesn’t have a huge link to the cloud. There’s no way you can have a big computer server there with huge batteries to run it.”
“You don’t have a huge internet connection to transmit the data to the cloud where it can be processed. All your computation has to happen right there.”
According to Agarwal, the development of TinyML could lead to even more innovation, as more sensors in buildings, infrastructure, vehicles, and personal devices record and compute. TinyML has the potential to support multiple industries, such as energy companies with sensors that monitor pipelines, aircraft makers that have sensors on actuators on planes, and the technology behind self-driving cars.
Therefore, this machine learning course could be useful for engineers looking to upskill, whether they operate in IT, software, hardware, devices, or sensors.
“They might find it useful in terms of learning about applications of TinyML. Others may find it useful in terms of how to develop these applications.”
Machine learning is quickly being deployed by more and more organisations, and new and interesting ways to utilise the technology are being discovered every day.
For example, online retailer Zappos uses analytics and machine learning to provide personalized sizing and search results for their customers.
According to CIO, “Using Amazon SageMaker, Zappos created models to predict customer apparel sizes, which are cached and exposed at runtime via microservices for use in recommendations. The system enabled single-digit millisecond response times and can handle more than 10 trillion requests per day.”
To avoid getting left behind, engineers and tech professionals should look into upskilling into niche machine learning courses like this latest one by Google and Harvard, so that they will be at the frontier of innovation in the years to come.