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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Innovations in deep learning (DL), especially the rapid growth of large language models (LLMs), have taken the industry by storm. DL models have grown from millions to billions of parameters and are demonstrating exciting new capabilities. …
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Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of …
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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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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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Deep learning (DL) models have been increasing in size and complexity over the last few years, pushing the time to train from days to weeks. Training large language models the size of GPT-3 can take months, leading to an exponential growth …
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Amazon’s product search engine indexes billions of products, serves hundreds of millions of customers worldwide, and is one of the most heavily used services in the world. The Amazon Search team develops machine learning (ML) technology …
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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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