Category: Amazon Machine Learning

With an aim to make the overwhelming variety of master data simple to consume, Bernhard Bicher and Tobias Widmer founded Onedot in 2014 in Zürich Switzerland, with a focus on processing structured and semi-structured product master data.

There are two key types of market participants; those who are trying to make money from the markets and those who are assigned to police those trying to make money. Examples of the former type include investment banks, hedge funds and asset managers, while examples of the latter includes in-house compliance, financial regulators and exchange surveillance teams.

Before the propagation of machine learning (ML) and AI to the general public, the news curating services were expensive, meaning they were largely out-of-reach for the average retail investor. Now, however, machine learning and Big Data are changing the game again.

On Wednesday, April 18th, nearly 200 attendees gathered at the AWS Loft for the second annual AWS Startup Day in San Francisco.
Missed any of the sessions or want to watch again? We’ve got you covered!

Founded in 2015, Nauto is focused on building a data platform to make vehicles on our roads safer while also improving the technology for autonomous vehicles in the future.

These days, distributed teams have plenty of enterprise software options that make collaborating feel seamless. But actual, face-to-face, IRL workplaces have been somewhat left behind. That’s where Density comes in. The San Francisco-based startup has built a piece of enterprise hardware for corporate campuses that measures how space is used. Many believe the technology is key to optimizing how all physical space is used.

Designed with startup founders and technical staff in mind, the AWS Startup Day series was created to help accelerate startup innovation and growth and we’re excited to be bringing the event to London for the first time.

Readaar co-founder Matthijs van Til explains how his startup, which buys imagery and sells data, learned how to sell their product to local governments.

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.

Assigning credit risk to people who apply for business loans, credit cards, and home mortgages has mostly been done by weighing some 10 or 20 attributes. Take those attributes—and we’re all familiar with some of the things that make our credit scores rise and fall, including timeliness of payments, debt to income ratio, and defaults—and crank them through your favorite logistic regression model or scorecard. The result is your assigned credit risk, and depending on the number, you either get your loan or you don’t.