AWS HPC Blog

Category: AWS Batch

Improving NFL player health using machine learning with AWS Batch

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%!

Streamlining distributed ML workflow orchestration using Covalent with AWS Batch

Streamlining distributed ML workflow orchestration using Covalent with AWS Batch

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.

Benchmarking the Oxford Nanopore Technologies basecallers on AWS

Oxford Nanopore sequencers enables direct, real-time analysis of long DNA or RNA fragments. They work by monitoring changes to an electrical current as nucleic acids are passed through a protein nanopore. The resulting signal is decoded to provide the specific DNA or RNA sequence by virtue of  compute-intensive algorithms called basecallers. This blog post presents the benchmarking results for two of those Oxford Nanopore basecallers — Guppy and Dorado — on AWS. This benchmarking project was conducted in collaboration between G42 Healthcare, Oxford Nanopore Technologies and AWS.