AWS Machine Learning Blog

JUNE 8, 2023

However, businesses can meet this challenge while providing personalized and efficient customer service with the advancements in generative artificial intelligence (generative AI) powered by large language models (LLMs). Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

Optimizing training with NVIDIA Tensor Core GPUs Gaining access to an NVIDIA Tensor Core GPU for large language model training is not enough to capture its true potential. Prior to working at Amazon Music, Siddharth was working at companies like Meta, Walmart Labs, Rakuten on E-Commerce centric ML Problems.

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AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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AWS Machine Learning Blog

APRIL 29, 2024

Historically, natural language processing (NLP) would be a primary research and development expense. In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows.

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AWS Machine Learning Blog

MAY 15, 2024

Acting as a model hub, JumpStart provided a large selection of foundation models and the team quickly ran their benchmarks on candidate models. After selecting candidate large language models (LLMs), the science teams can proceed with the remaining steps by adding more customization.

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The MLOps Blog

JULY 18, 2023

I think another huge component, as I kind of was mentioning earlier, is Conversational AI tends to require large pipelines of machine learning. You usually cannot do a one-shot, “here’s a model,” then it handles everything no matter what you’re reading today. And so we actually need to have a full pipeline of models.

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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