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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Matt Rasmussen, VP of Software Engineering at insitro, expands on his first post on redun, insitro’s data science tool for bioinformatics, to describe how redun makes use of advanced AWS features. Specifically, Matt describes how AWS …
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Matt Rasmussen, VP of Software Engineering at insitro describes their recently released, open-source data science framework, redun, which allows data scientists to define complex scientific workflows that scale from their laptop to …
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