AWS Innovation Storage 
AWS Machine Learning - AI
MARCH 4, 2025
To achieve these goals, the AWS Well-Architected Framework provides comprehensive guidance for building and improving cloud architectures. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure. This systematic approach leads to more reliable and standardized evaluations.
AWS Machine Learning - AI
OCTOBER 17, 2024
During re:Invent 2023, we launched AWS HealthScribe , a HIPAA eligible service that empowers healthcare software vendors to build their clinical applications to use speech recognition and generative AI to automatically create preliminary clinician documentation. AWS HealthScribe will then output two files which are also stored on Amazon S3.
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AWS Machine Learning - AI
NOVEMBER 13, 2024
Recognizing this need, we have developed a Chrome extension that harnesses the power of AWS AI and generative AI services, including Amazon Bedrock , an AWS managed service to build and scale generative AI applications with foundation models (FMs). The user signs in by entering a user name and a password.
AWS Machine Learning - AI
DECEMBER 24, 2024
To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. Its mounted at /fsx on the head and compute nodes.
AWS Machine Learning - AI
NOVEMBER 6, 2024
To maintain their competitive edge, organizations are constantly seeking ways to accelerate cloud adoption, streamline processes, and drive innovation. Readers will learn the key design decisions, benefits achieved, and lessons learned from Hearst’s innovative CCoE team. This post is co-written with Steven Craig from Hearst.
AWS Machine Learning - AI
NOVEMBER 13, 2024
Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. The following diagram illustrates the solution architecture: The steps of the solution include: Upload data to Amazon S3 : Store the product images in Amazon Simple Storage Service (Amazon S3).
AWS Machine Learning - AI
NOVEMBER 7, 2024
In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach.
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