Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale …
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AWS is developing new tools to enable easier and faster deployment of level 3 digital twin virtual sensors. in this post we show why L3 digital twins are needed for virtual sensors and how to elastically deploy one the cloud at scale. Read …
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Today we are launching I4g instances powered by AWS Graviton2 processors that deliver up to 15% better compute performance than our other storage-optimized instances. With up to 64 vCPUs, 512 GiB of memory, and 15 TB of NVMe storage, one of …
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In this post, we show how tools from our partner Good Chemistry can help you further explore the potential of quantum computing for non-trivial molecular systems, and design experiments that you can run on Amazon Braket. Read the Post on …
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New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. Combined with their flexibility, high speed of development, and low operating cost, these …
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In this blog post, we show how you can track and manage hybrid quantum-classical experiments with the Hybrid Jobs feature in Amazon Braket and Amazon SageMaker Experiments, used by ML developers on AWS to organize their experiments and …
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Many applications leverage distributed task systems like Celery to handle asynchronous work. In this post, we describe how to handle compute-intensive Celery tasks using AWS Batch to scale the compute resources and run worker agents. Read …
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We always worry the most about the compute nodes on a cluster (for a good reason - it’s where all the action is), but it’s worth taking a moment to think about your head node, because that’s where you can really define …
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