Category: Artificial Intelligence

Learn how to deploy AI models at scale with @AWS using NVIDIA’s NIM and Amazon EKS! This step-by-step guide shows you how to create a GPU cluster for inference in this second post of a two-part series!

Transcribe audio files at scale for really low cost using Whisper and AWS Batch with Inferentia. Check out this post to deploy a cost-effective solution in minutes!

Learn how to deploy AI models at scale with @AWS using NVIDIA’s NIM and Amazon EKS! This step-by-step guide shows you how to create a GPU cluster for inference. Don’t miss part 1 of this 2-part blog series!

AWS Batch multi-node parallel jobs can now run on Amazon EKS to provide gang scheduling of pods across nodes for large scale distributed computing like ML model training. More details here.

Launching distributed GPT training? See how AWS ParallelCluster sets up a fast shared filesystem, SSH keys, host files, and more between nodes. Our guide has the details for creating a Slurm-managed cluster to train NeMo Megatron at scale.

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.

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.

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%!

In this blog post, we catch a glimpse into drug discovery to see how Evolvere Biosciences has deployed a customized architecture w/ AWS Batch and Nextflow to quickly and easily run its macromolecule design pipeline.

Matt Rasmussen, VP of Software Engineering at insitro, expands on his first post on redun, insitro’s data science tool for bioinformatics, to describe how redun makes use of advanced AWS features. Specifically, Matt describes how AWS Batch’s Array Jobs is used to support workflows with large fan-out, and how AWS Glue’s DynamicFrame is used to run computationally heterogenous workflows with different back-end needs such as Spark, all in the same workflow definition.