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 …
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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 …
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To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between …
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Since launch, EFA has seen continuous improvements in performance. In this post, we talk about our 2nd generation of EFA, which takes another step in improving Machine Learning and High Performance Computing in the Cloud. Read the Post on …
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AWS Batch is a fully-managed AWS service that orchestrates vast numbers of jobs using containers. It leverages some of your favorite container systems - Amazon ECS and Kubernetes, and - above all: makes your life easier because it handles …
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Pay-as-you-go resources are a compelling but budget-limited researchers performing HPC workloads need help working within the bounds of their grants. In this post, we show how to build a real-time cost guardian for AWS Batch to help enforce …
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Amazon SageMaker is a fully-managed service that provides every developer and data scientist with the ability to quickly build, train, and deploy machine learning (ML) models at scale. ML is realized in inference. SageMaker offers four …
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Since it was launched in 2017, AWS Batch has used the Amazon Elastic Container Service (Amazon ECS) to create the clusters it uses for deploying container jobs. That included AWS Fargate, which is a serverless face to ECS. But starting …
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