Category: Customer Solutions

Discover how Petrobras and Universidade Federal Fluminense (Rio de Janeiro — Brazil) developed an innovative HPC solution on AWS, leveraging Spot Instances to optimize costs. Explore the automations in place to avoid interruptions and use the lowest-cost instances available.

Discover how a new simulation-based approach using agent-based modeling, rich data, and advanced analytics can help you build resilient supply chains that withstand disruptions. Learn more in our latest post.

Balyasny Asset Management (BAM), a $22B global investment firm, faced a unique challenge: how to empower 160 investment teams to conduct cutting-edge research across six strategies. Discover how they leveraged AWS Batch and Amazon EKS to supercharge their research capabilities.

This post was contributed by Eran Brown, Principal Engagement Manager, Prototyping Team, Vedanth Srinivasan, Head of Solutions, Engineering & Design, Edmund Chute, Specialist SA, Solution Builder, Priyanka Mahankali, Senior specialist SA, Emerging Domains For engineering companies, the cost of Computer Aided Design and Engineering (CAD/CAE) tools can as high as 20% of product development cost. […]

Optimizing workloads for performance and cost-effectiveness is crucial for businesses of all sizes – and especially helpful for workloads in the cloud, where there are a lot of levers you can pull to tune how things run. AWS offers a vast array of instance types in Amazon Elastic Compute Cloud (Amazon EC2) – each with […]

Managing extensive HPC workflows? This post details how to monitor resource consumption without compromising security. Check it out for a customizable reference architecture that sends only relevant data to your monitoring account.

In this post, we share how Amazon Search optimizes their use of accelerated compute resources using AWS Batch fair-share scheduling to schedule distributed deep learning workloads.

In this post we’ll show you how the NFL used AWS to scale their ML workloads and produce the first comprehensive dataset of helmet impacts across multiple NFL seasons. They were able to reduce manual labor by 90% and the results beats human labelers in accuracy by 12%!

AWS TwinFlow is an open source framework to build and deploy predictive models using heterogenous compute pipelines on AWS. In this post, we show the versatility of the framework with examples of engineering design, scenario analysis, systems analysis, and digital twins.

Complicated multi-step workflows can be challenging to deploy, especially when using a variety of high-compute resources. Covalent is an open-source orchestration tool that streamlines the deployment of distributed workloads on AWS resources. In this post, we outline key concepts in Covalent and develop a machine learning workflow for AWS Batch in just a handful of steps.