AWS HPC Blog
Tag: Machine Learning
Amazon’s renewable energy forecasting: continuous delivery with Jupyter Notebooks
Interested in eliminating friction between data science and engineering teams? Read this post to learn how Amazon successfully transitioned Jupyter Notebooks from the lab to production.
Leveraging Seqera Platform on AWS Batch for machine learning workflows – Part 2 of 2
In this second part of using Nextflow for machine learning for life science workloads, we provide a step-by-step guide, explaining how you can easily deploy a Seqera environment on AWS to run ML and other pipelines.
Enhancing ML workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks for ML
No more guessing if GPU capacity will be available when you launch ML jobs! EC2 Capacity Blocks for ML let you lock in GPU reservations so you can start tasks on time. Learn how to integrate Caacity Blocks into AWS ParallelCluster to optimize your workflow in our latest technical blog post.
EFA: how fixing one thing, led to an improvement for … everyone
Today, we’re diving deep into the open-source frameworks that move MPI messages around, and showing you how work we did in the Open MPI and libfabrics community lead to an improvement for EFA users – and everyone else, too.
Conceptual design using generative AI and CFD simulations on AWS
In this post we’ll show how generative AI, combined with conventional physics-based CFD can create a rapid design process to explore new design concepts in automotive and aerospace from just a single image.
How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch
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.
How computer vision is enabling a circular economy
In this post, we show how Reezocar uses computer vision to change the way they detect damage and price used vehicles for re-sale in secondary markets. This reduces landfill and helps achieve the goals of the circular economy.
Improving NFL player health using machine learning with AWS Batch
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%!
How to make digital technologies for the circular economy work for your business
In this post, we discuss the benefits of digital technology for the circular economy, and show how businesses can implement these technologies to get the most out of them for the wellbeing of everyone.
Rigor and flexibility: the benefits of agent-based computational economics
In this post, we describe Agent-Based Computational Economics (ACE), and how extreme scale computing makes it beneficial for policy design.