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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Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by …
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In this blog post, we catch a glimpse into drug discovery to see how Evolvere Biosciences has deployed a customized architecture w/ AWS Batch and Nextflow to quickly and easily run its macromolecule design pipeline. Read the Post on the AWS …
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Batch inference is a common pattern where prediction requests are batched together on input, a job runs to process those requests against a trained model, and the output includes batch prediction responses that can then be consumed by other …
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Running Jupyter notebooks as Hybrid Jobs on Amazon Braket, you get performance and convenience of jobs, without modifying code. In this post, we show how you can scale up from exploratory notebook to repeatable and reliable experiments on …
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