Tag: Amazon EC2

Predictive maintenance solutions involve using artificial intelligence (AI) algorithms and data analytics tools to monitor operations, detect anomalies, and predict possible defects or breakdowns in equipment before they happen. To help keep aircraft mission ready, the Air Force turned to PavCon, LLC, (PavCon), a woman-owned small business, to create an actionable predictive maintenance solution powered by Amazon Web Services (AWS).

Using AWS, Edunation seamlessly responded to increasing demand during the COVID-19 pandemic. Edunation collaborates with top educational institutions across the Middle East and North Africa (MENA) region and provides all-in-one learning and school management solutions. Today, the EdTech is on a mission to push learning management systems (LMS) beyond virtual classrooms.

About 40 million MRI scans are performed in the United States every year. MRIs are a valuable part of diagnostic plans, but as they exist today, they may not always be a part of a patient’s care plan. A research team at the New York University (NYU) Langone Center set out to make MRIs more accessible for more patients by using artificial intelligence (AI), machine learning (ML), and the power of cooperative open data sharing.

“Action cannot be completed because the system is out of date.” Every technology user understands the frustration of getting this message. When Bucknell University turned to the cloud to modernize their Enterprise Resource Planning (ERP) system, they found unexpected gifts along the way: more cost savings, better solutions, and best of all, new “found” time to devote to high impact projects.

Computational biology is undergoing a revolution. However, the analysis of single cells is a hard problem to solve. Standard statistical techniques used in genomic analysis fail to capture the complexity present in single-cell datasets. Open Problems in Single-Cell Analysis is a community-driven effort using AWS to drive the development of novel methods that leverage the power of single-cell data.

This is a guest post by Maria Teleńczuk, research engineer at the Paris-Saclay Center for Data Science (CDS), and Alexandre Gramfort, senior research scientist at INRIA, the French National Institute for Research in Digital Science and Technology. Maria and Alexandre explain how they adapted their open source data challenge platform RAMP to train the models submitted by student challenge participants using Amazon Elastic Compute Cloud (Amazon EC2) Spot instances, and how they leveraged AWS to support three student challenges.

OCEARCH is a data-centric organization built to help scientists collect previously unattainable data about the ocean. Their mission is to accelerate the ocean’s return to balance and abundance, through innovation in scientific research, education, outreach, and policy, using unique collaborations of individuals and organizations in the US and abroad. As part of the Amazon Sustainability Data Initiative (ASDI), we invited Fernanda Ubatuba, president and COO at OCEARCH, to share how her organization is making strides in helping ocean conservation and how AWS is supporting her mission.

The Urban Analytics Lab at the National University of Singapore (NUS) spearheads research in geospatial data analysis and 3D city modelling. The lab’s work underpins the development of smart cities and provides scientists, architects, urban planners, and real estate developers with data insights. These insights help parties make informed decisions about projects ranging from energy modelling to urban farming. To meet rising global demand for its data analytics and planning tools, Urban Analytics Lab turned to Amazon Web Services (AWS).

One of the biggest scientific achievements of the twenty-first century was the completion of the Human Genome Project and the publication of a draft human genome. The project took over 13 years to complete and remains one of the largest private-public international collaborations ever. Advances since in sequencing technologies, computational hardware, and novel algorithms reduced the time it takes to produce a human genome assembly to only a few days, at a fraction of the cost. This made using the human genome draft for precision and personalized medicine more achievable. In this blog, we demonstrate how to do a genome assembly in the cloud in a cost-efficient manner using ARM-based AWS Graviton2 instances.

In the spring of 2019, environmental modelers at the Lake Michigan Air Directors Consortium (LADCO) had a new problem to solve. Emerging research on air pollution along the shores of the Great Lakes in the United States showed that to properly simulate the pollution episodes in the region we needed to apply our models at a finer spatial granularity than the computational capacity of our in-house HPC cluster could handle. The LADCO modelers turned to AWS ParallelCluster to access the HPC resources needed to do this modeling faster and scale for our member states.