Category: *Post Types

After careful consideration, we have made the decision to discontinue NICE EnginFrame including NICE EnginFrame views, effective September 25, 2025. If you want to continue using NICE EnginFrame beyond the end-of-support date, we recommend contacting NI-SP, an AWS partner with decades of experience implementing and supporting NICE EnginFrame for enterprises.

See how agent-based models, driven to scale by HPC in the cloud, are shedding new light on macroprudential policies with this post from Oxford’s Institute for New Economic Thinking.

If you work in the semiconductor industry with electronic design automation tools and workflows, this guide will help you build an HPC cluster on AWS specifically configured for your needs. It covers AWS ParallelCluster and customizations specifically to cater to EDA.

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.

If you want to build enterprise-grade HPC on AWS, what’s the best path to get started? Should you create a new AWS account and build from scratch? In this post we’ll walk you through the best practices for getting setup cleanly from the start.

Today we discuss the performance results we saw from the new hpc7a instance, running HPC workloads like CFD, molecular dynamics, and weather prediction codes.

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%!

Today we dive into details of AWS Batch fair share policies and show how they affect job placement. You’ll see the result of different share policies, and hear about practical use cases where you can benefit from fair share job queues in Batch.

Today we’re going to show you how you can automate cluster deployment and create self-documenting infrastructure at the same time, which leads to more repeatable results that are easier to manage (and replicate).

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.