Category: Amazon Machine Learning

Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.

Citizens want to report issues to their local governments in a fast and simple manner and not have to worry about identifying the right government agency or phone number—for instance, if a fire hydrant is broken, or a road sign has fallen over. In this blog post, learn how to set up a solution with AWS deep learning services that creates a fluid experience for reporting and addressing these issues.

The Busan Cloud Innovation Center (CIC) teamed up with the World Food Programme (WFP), the Busan IT Promotion Agency, and technology startup Nuvilab to use cloud technology to address food waste in South Korea. Using Amazon’s “Working Backwards” approach with artificial intelligence (AI), machine learning (ML), and data lake solutions on AWS, the team developed Zero Waste Zero Hunger (ZWZH), a program that uses artificial intelligence AI to provide data about food consumption.

Healthcare providers and healthcare systems want to modernize their healthcare data exchanges so they can better analyze and gain more insight from their clinical data. In this walkthrough, learn how to use AWS to migrate legacy healthcare messaging data into Amazon HealthLake, which can use artificial intelligence (AI) and machine learning (ML) to discover meaningful and actionable healthcare information embedded in unstructured text.

Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.

The cloud is changing the way we do research—accelerating the pace of innovation, democratizing access to data, and allowing researchers and scientists to scale, work collaboratively, and make new discoveries from which we may all benefit. Researchers from around the world look to the AWS Cloud for customer-focused, pioneering, and secure solutions for their toughest challenges. Discover how customers in Latin America and Canada use AWS for research.

The Amazon re:MARS 2022 conference brought together thought leaders, technical experts, and groundbreaking companies and organizations that are transforming what’s possible in machine learning (ML), automation, robotics, and space. Advancements in these fields are the engines that will drive innovation for the next 100 years. Read on to learn about announcements from re:MARS related to the public sector, plus some of the innovative organizations and companies that were onsite to inspire guests with breakthrough technologies and ideas.

Artificial intelligence (AI) and machine learning (ML) are critical tools being used in healthcare research, autonomous applications, predictive maintenance, and also a key tool used to advance sustainability solutions. However, to use AI and ML to solve sustainability problems, innovators need specific datasets that are prepared for analysis and training of the models. To help create and accelerate sustainability solutions, the Amazon Sustainability Data Initiative (ASDI) today announced easier identification of sustainability datasets with integration in AWS Data Exchange and the launch of a sustainability hackathon.

As AI and ML becomes more ubiquitous and simple to use, EdTech companies and startups around the world are unlocking the benefits of these technologies, particularly when it comes to personalizing learning content for a learner or educator’s specific needs. Learn how EdTech startups and AWS EdStart Members are using AI and ML to help revolutionize the education industry. 

Skillshare is the largest global online learning community for creativity. They offer thousands of inspiring classes for creative and curious people on topics including illustration, design, photography, video, freelancing, and more. Skillshare wanted their members to easily discover relevant content with a seamless discovery process of personalized recommendations. Skillshare decided to test Amazon Personalize from AWS to make these data-fueled recommendations for members with machine learning. This blog post describes their Amazon Personalize solution architecture, their AWS Step Functions process, and the results of their experiment.