Category: Compute

The University of Minnesota Athletics Department had access to a lot of data, but lacked a way to bring it together. Even answering simple questions, like how many tickets were sold and who received them, required hours of cross-platform data wrangling. In less than a year, the department replaced that complexity with a scalable data lake built on AWS. The new data architecture—which gives the department full visibility into ticketing transactions and digital behavior—was built by a small internal team, without requiring a complete overhaul of their existing systems.

This significant growth in military unmanned aerial vehicles, which serve as a proxy for the broader autonomous systems market, underscores the urgent need for scalable, secure, and efficient software deployment solutions. As the number of autonomous platforms increases, so does the complexity of managing their software systems. In response to this growing challenge, Shield AI and AWS have collaborated and successfully demonstrated a scalable architecture and proof of concept (PoC) that addresses challenges often seen when deploying autonomy software and machine learning algorithms onto autonomous systems.

Learning Upgrade worked with AWS to modernize its platform and ultimately migrated fully to the cloud. This strategic shift empowered Learning Upgrade to scale globally, respond faster to new opportunities, and better serve learners in dynamic, resource-constrained environments. Read this post to learn more.

In this post, we demonstrate how to use the advanced malware detection features of Amazon GuardDuty to uncover malicious and suspicious files compromising your Amazon Elastic Compute Cloud (Amazon EC2) instances. We use the investigative capabilities of Amazon Detective to gain deeper insights into the security event. After the key questions about the security event are addressed, we outline steps to remediate the potentially compromised EC2 instance.

This post demonstrates how to enhance security and compliance in an isolated SageMaker JupyterLab environment by implementing two key customizations: configuring download restrictions and implementing secure Python package installation through AWS CodeArtifact.

Technical Safety BC (TSBC) regulates the safe installation and operation of technical systems (electrical, gas, boiler, elevator, etc.) in British Columbia. This post showcases how the TSBC built a machine learning operations (MLOps) solution using AWS to streamline production model training and management to process public safety inquiries more efficiently.

Read this post to learn how Change.org built a next-generation recommender on AWS that turned more awareness into action—boosting petition signatures by 30 percent.

AWS Professional Services has partnered with the National Environmental Satellite, Data, and Information Service (NESDIS), part of the National Oceanic and Atmospheric Administration (NOAA), alongside GAMA-1 Technologies to broaden the scope and capabilities of the NESDIS Common Cloud Framework (NCCF). This work supports NOAA’s ability to ingest, manage, process, and disseminate critical environmental data. Read this post to learn more.

This post demonstrates how to build serverless workflows on AWS that process such data using AWS Step Functions and integrate with downstream systems that have concurrency limitations.

The Greenwood Genetic Center (GGC) is a nonprofit institute organized to provide clinical genetic services, diagnostic laboratory testing, educational programs and resources, and research in the field of medical genetics. To make its medical records accessible to providers, the GGC created a data warehouse to serve as a single repository for its data, where clinicians have access to an all-encompassing view of patients’ records. Read this post to learn more.