Organizations in regulated industries are frequently required to maintain a copy of their business-critical file data in another location to meet corporate compliance or disaster recovery (DR) mandates. Furthermore, in highly distributed …
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Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and …
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Today, we are announcing the availability of Amazon EFS Elastic Throughput, a new throughput mode for Amazon EFS that is designed to provide your applications with as much throughput as they need with pay-as-you-use pricing. This new …
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Training Machine Learning models involves multiple steps, it gets more complex and time consuming when the size of the data set for training is in the range of 100s of GBs. Data Scientists run through large number of experiments and …
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Stateful applications rely on data being persisted and retrieved to run properly. When running stateful applications using Kubernetes, state needs to be persisted regardless of container, pod, or node crashes or terminations. This requires …
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When performing an online data migration, an important requirement is often security in transit. When evaluating migration options, you should consider if the tools available can provide encryption of data in flight, to help prevent …
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If you are new to AWS and trying to figure out which Storage services to use, how to use them, and best practices, it can be challenging to find guidance that you can trust. The depth of features and options means that even experienced …
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In this post, we demonstrate how Kubeflow on AWS (an AWS-specific distribution of Kubeflow) used with AWS Deep Learning Containers and Amazon Elastic File System (Amazon EFS) simplifies collaboration and provides flexibility in training …
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