Dealing with data on the edge
Who is this presentation for?Data engineers, data architects, developers
Today much of the data we collect is thrown away. The amount of data that makes its way back to the cloud is a lot smaller than the data that’s still out in the world, distributed, and scattered. This data isn’t in the cloud because it exists on systems that, at least right now, are unreliably connected to the network or not connected to the network in the first place.
Alasdair Allan explains why that’s about to change. We’re seeing seen a dramatic drop in the amount of computing power necessary to run machine learning models, and perhaps more crucially, we’ve seen a corresponding drop in the power budget needed to run those models. You can now do machine learning on a microcontroller board powered by a single coin cell battery that should last for months.
Data you used to discard due to cost, bandwidth, or a limited power budget, can now be processed in real time on hardware at the edge, and you’ll explore how that influences how you store, process, and deal with your data.
- General knowledge of machine learning techniques
- Experience programming with Python and TensorFlow (useful but not required)
What you'll learn
- Learn how machine learning on device allows data processing and decision making in real time without reference to the cloud
- Discover the current state of low-powered hardware capable of running machine learning models at the edge and architectures to implement machine learning models on that hardware
- See a basic example code to help you get started with machine learning on edge hardware
Babilim Light Industries
Alasdair Allan is a director at Babilim Light Industries and a scientist, author, hacker, maker, and journalist. An expert on the internet of things and sensor systems, he’s famous for hacking hotel radios, deploying mesh networked sensors through the Moscone Center during Google I/O, and for being behind one of the first big mobile privacy scandals when, back in 2011, he revealed that Apple’s iPhone was tracking user location constantly. He’s written eight books and writes regularly for Hackster.io, Hackaday, and other outlets. A former astronomer, he also built a peer-to-peer autonomous telescope network that detected what was, at the time, the most distant object ever discovered.
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