AWS Startups Blog

Category: AWS Deep Learning AMIs

How one Swiss startup used the cloud to achieve the same level of security as a Swiss Bank

Like many startups today, Nummo was born in the cloud and given our industry and our future goals, we wouldn’t compromise any other aspects of our offering. People needed to feel their data is secure when using Nummo.

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What Is Deep Learning? A Primer with Bonsai’s Mark Hammond

Over the past few years, the tech conversation has been dominated by references to AI and deep learning. Some people liken it to websites in 1995—everybody knew they needed one, but they didn’t all know what they were. Today, there are a lot of people talking about AI without having a real grasp on how deep learning works or even what it means. So what is it, really?

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The Smallpdf team at their offices in Zurich, Switzerland.

How to run a world-class website with a DevOps team of two

At last count, Smallpdf, the PDF conversion startup I work for, had roughly 13 million monthly users. As for the number of employees currently running our website? That would be 10—with only two employees focused on the backend and infrastructure. You might be curious to learn how we run such a processing intensive website with such a small DevOps team. Our little secret stands in automation and delegation.

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The Robocar Rally track at AWS re:Invent 2017.

Baby, You Can Drive Your Own Car: A Look at How Deep Learning Powered AWS’s Robocar Rally

What’s more fun than an all-night hackathon in Vegas? Why, a Vegas hackathon involving self-driving cars, of course. During AWS’s 2017 re:Invent conference, 25 teams were given kits to build an autonomous car that could learn how to drive around a track on its own. In this video, we explain what deep learning has to do with autonomous driving and how even a light-hearted Robocar Rally can have deeper implications for learning and the cloud.

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A screenshot of the Visipedia Bird app.

Caltech’s Pietro Perona on how deep learning can help you classify birds and trees

Imagine, for a moment, that you discover an irregular mole on yourself. Whereas nowadays you still need to take time to schedule a dermatological appointment and then wait weeks to get your test results, Caltech Professor Pietro Perona is eagerly awaiting the day when you can snap a picture of the mole with your smartphone and then learn instantaneously if it’s dangerous or not. And he should know. As the co-creator, with Cornell Tech Professor Serge Belongie, of the AI and machine learning-based visual classification system Visipedia, Perona has spent the past seven years working on a “switchboard” that lets anyone, everywhere ask questions and immediately obtain an answer.

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