In this final part of this three-part blog series on building predictive models at scale in AWS, we will use the synthetic dataset and the models generated in the previous post to showcase the model updating and sensitivity analysis …
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In the first part of this three-part blog series, we introduced the aws-do-pm framework for building predictive models at scale in AWS. In this blog, we showcase a sample application for predicting the life of batteries in a fleet of …
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Predictive models have powered the design and analysis of real-world systems such as jet engines, automobiles, and powerplants for decades. These models are used to provide insights on system performance and to run simulations, at a …
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Spack has already been removing the ugly work from building HPC codes, but with the announcement of the Spack Binary Cache at ISC'22, build and deploy times for these complicated applications will drop by 95% or more in most cases.
Greg …
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Estimating the usage and cost of experiments is a key requirement for customers interacting with Amazon Braket. Customers want to have a way to track cost and usage as their algorithms run, especially for complex problems that require …
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With the Graviton Challenge last year, we helped customers migrate to Graviton-based EC2 instances and get up to 40 percent price performance benefit in as little as 4 days. Tens of thousands of customers, including 48 of the top 50 Amazon …
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In this blog post, we discuss the AWS solution that Amazon’s construction division used to conduct large-scale CFD fire simulations as part of their Fire Strategy solutions to demonstrate safety and fire mitigation strategies. We outline …
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After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized …
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