AWS for Industries

Category: Analytics

4 surprising restaurant industry trends

In the previous blog post 7 defunct restaurant brands we can learn from, I reviewed how some historic restaurant brands failed to adjust to changing consumer preferences and market dynamics. This post examines the restaurant industry trends that we are watching for in 2020 and how AWS prepares restaurants for the impact. Trend 1: The […]

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How to process medical text in multiple languages using Amazon Translate and Amazon Comprehend Medical

Amazon Comprehend Medical is a HIPAA eligible service that uses deep learning to identify and extract relevant information from medical text. The service uses trained Natural Language Processing (NLP) models to identify medical entities and relationships, such as medication, dosage, diagnosis, and Protected Health Information (PHI). This provides an efficient and cost-effective way to mine data […]

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Restaurants are hungry for data insights as food delivery analytics gains traction

This is a point of view from Wavicle — an AWS Select Tier Consulting Partner specialized in custom data, analytics, and cloud solutions. “Eating out is fun.” You may or may not remember that nostalgic tagline from decades ago. Now there’s a modern 21st century corollary to it: “Eating in is fun.” Whether at home, […]

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Executive Conversations: Jonathan Barouch, CEO, Local Measure

Local Measure is making travel and hospitality more human through technology. CEO of Local Measure Jonathan Barouch joined David Peller, Head of Worldwide Business Development for AWS Travel and Hospitality, for a wide-ranging discussion that uncovered insights into the industry and current landscape. — David Peller (DP): Tell me a little about your business, Local […]

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7 defunct restaurant brands we can learn from

Regardless of your age, it is likely that you have fond memories of visiting a restaurant brand that is no longer in existence. That restaurant may no longer be around because they likely failed to adjust to changing consumer preferences and market dynamics. By learning from our past, we can avoid repeating the same mistakes, […]

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AWS re:Invent 2019 – Healthcare and life sciences industry recap

With over 75 launches and announcements of new services and major features during re:Invent, it can be hard for any technologist to keep track of the most relevant information for Healthcare and Life Science (HCLS) workloads. Explore the below recap of information important for HCLS customers and links to the top re:Invent breakout sessions. Top […]

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AWS re:Invent 2019: Key announcements for the Advertising and Marketing Industry

With more than 77 new service launches and other announcements, AWS re:Invent left a lot to digest—even for teams inside AWS! We’ve developed this guide to ensure customers in the advertising and marketing industry know the most relevant news from this year’s event. Of the many announcements, three stood out as game-changers for advertising and […]

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Predict patient health outcomes using OHDSI and machine learning on AWS

Build a machine learning model to predict the likelihood of stroke in a patient with newly diagnosed atrial fibrillation In healthcare, patient outcome prediction is a critical step in improving the effectiveness of care delivery while reducing its overall cost.  Being able to accurately forecast what will happen next to patients, at scale, is key […]

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A Travel Tech CEO’s journey from cloud skeptic to evangelist

As CEO of 3Victors, a travel big data AI startup, who spent the better part of his career solving tricky digital problems by tuning software to squeeze maximum performance from racks of bare metal computers, I was more than a bit skeptical of cloud computing. First, let me provide a little insight into my company […]

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AWS Public Database program adds valuable MIMIC-III dataset for researchers

Biomedical researchers require access to accurate, detailed data. The MIT Laboratory of Computational Physiology (LCP) MIMIC-III dataset is a popular resource that captures a variety of measures longitudinally over time, across many patients, and can drive analytics and machine learning toward research discovery and improved clinical decision-making. Recently, the MIT Laboratory of Computational Physiology (LCP) […]

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