Category: 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%!

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.

In this post we explore SeatGeek’s load testing system that simulates 50k simultaneous users. Originally built to prep SeatGeek for large-event traffic spikes, it now runs weekly to help them harden their code.

In this post, we discuss how Novo Nordisk approached the deployment of a scale-out HPC platform for running AlphaFold, while meeting their enterprise IT requirements and keeping the user experience simple.

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.

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.

Many applications leverage distributed task systems like Celery to handle asynchronous work. In this post, we describe how to handle compute-intensive Celery tasks using AWS Batch to scale the compute resources and run worker agents.

Today we show you how to get insights into the costs of running AWS Batch workloads on Amazon EKS using Kubernetes pod labels with Kubecost.

In this post, AWS Professional Services highlights how they helped data scientists from Amazon Logistics rearchitect their algorithm for improving the efficiency of their supply-chain by making better planning decisions. Leveraging best practices for deploying scalable HPC applications on AWS, the teams saw a 4X improvement in run time.

AWS Batch customers often ask for guidance to optimize their architectures and make their workload to scale rapidly. Here we describe an observability solution that provides insights into your AWS Batch architectures and allows you to optimize them for scale and quickly identify potential throughput bottlenecks for jobs and instances.