AWS Startups Blog

Tag: Machine Learning

DISCO Transforms the Practice of Law Using AWS and Serverless Computing

Austin-based legal technology leader DISCO is on a mission to reinvent the practice of law through software by making lawyers more efficient in everything they do. Founded in 2013, it has revolutionized the way law firms and corporate legal departments operate, using technology and cutting-edge AI to analyze data quickly and free up resources for tasks that require legal judgment. DISCO provides a key competitive advantage in an industry where speed and accuracy are critical.

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Olive Builds the Internet of Healthcare and an AI Workforce on AWS

Today, the healthcare industry is flooded with software. Any given hospital has an EMR, billing software, different portals for every insurance partner, and individual medical tools each with their own interfaces, just to name a few. None of these systems work together, and the downstream effects dehumanizes the care experience. Olive is designed to connect these disparate parts, shining a new light on old processes, connecting providers delivering care and payers reimbursing that care to ultimately drive a better patient experience.

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digital content creation startup Renderro

Causality Link Uses Amazon Translate to Bring in Global Perspectives

As an investor, Eric Jensen, co-founder and CTO of Causality Link, was frustrated with how difficult and time consuming it was to project trends in financial markets. Too often, he found there was either no information available or only regurgitated sources, and he decided to change how investors consume information to make decisions. Eric started Causality Link to empower investor decisions with natural language processing (NLP) and provide information from around the globe in a consolidated and interactive platform.

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Flo: Advancing Women’s Health with ML and Amazon SageMaker

The body is often a mystery, and it’s nice when an external source is able to provide expert, evidence-based information about it. Flo App, a holistic health and wellbeing platform that helps women understand their bodies and minds, was built to do just that. Founded in 2015, Flo supports women as they make better informed decisions about their reproductive, physical, and mental health.

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Car Sales Startup Kavak Kicks ML into High Gear with AWS and a Serverless Architecture

Founded in 2016, Kavak is the digital platform that’s making it easier than ever to buy and sell cars. The Mexico City-based founded startup recently achieved “unicorn” status after reaching a $1.15 billion valuation, the first tech company in the country to do so. As Kavak expands its operations to Argentina and sets sights on Brazil, we sit down with Vice President of Data Science, Anders Christiansen, to chat about how machine learning and AWS serverless services helped build the engine behind the company’s ever-improving workflow.

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Onkolyze: Leveraging ML to Offer Personalized Cancer Treatment

The fight against breast cancer has made progress over the past two decades thanks to advances in treatment and screening. But while mortality rates from the disease have fallen for women over 50, they remain frustratingly steady for younger women. Onkolyze, a startup based in Singapore, is hoping to help solve just that problem by applying high-powered AWS GPUs and ML, making early detection much easier.

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How Startups Deploy Pretrained Models on Amazon SageMaker

For most machine learning startups, the most valuable resource is time. They want to focus on developing the unique aspects of their business, not managing the dynamic compute infrastructure needed to run their applications. Productionizing machine leaning should be easier, and that’s where AWS comes in. In this blog post and corresponding GitHub repo, you will learn how to bring a pre-trained model to Amazon SageMaker to have production-ready model serving in under 15 minutes.

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Understanding the New World of Office Space with Basking

Overnight, the COVID-19 pandemic reshaped how and where Americans work. By June, according to a survey from Stanford researchers, 42% of the U.S. labor force was working from home full time, with millions more not working at all. For employers, that shift has led to new challenges as they navigate an unprecedented economy. One big question: what to do with all the empty offices?

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