AWS Storage Blog

Category: Amazon SageMaker

A gene-editing prediction engine with iterative learning cycles built on AWS

NRGene develops cutting-edge genomic analytics products that are reshaping agriculture worldwide. Among our customers are some of the biggest and most sophisticated companies in seed-development, food and beverages, paper, rubber, cannabis, and more. In the middle of 2020, NRGene joined a consortium of companies and academic institutions to build the best-in-class gene-editing prediction platform to […]

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Using high-performance storage for machine learning workloads on Kubernetes

Organizations are modernizing their applications by adopting containers and microservices-based architectures. Many customers are deploying high-performance workloads on containers to power microservices architecture, and require access to low latency and high throughput shared storage from these containers. Because containers are transient in nature, these long-running applications require data to be stored in durable storage. Amazon FSx […]

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New on the Machine Learning blog: Speed up training on Amazon SageMaker using Amazon FSx for Lustre and Amazon EFS file systems

Deploying analytics applications and machine learning models requires storage that can scale in capacity and performance to handle workload demands with high throughput and low-latency file operations. A common use case we’re seeing centers around data science teams doing some form of analytics (e.g machine learning, genomics). AWS offers two scalable, durable, highly available file […]

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