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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning - AI

Training large language models (LLMs) models has become a significant expense for businesses. PEFT is a set of techniques designed to adapt pre-trained LLMs to specific tasks while minimizing the number of parameters that need to be updated.

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Multi-LLM routing strategies for generative AI applications on AWS

AWS Machine Learning - AI

The Pro tier, however, would require a highly customized LLM that has been trained on specific data and terminology, enabling it to assist with intricate tasks like drafting complex legal documents. Before migrating any of the provided solutions to production, we recommend following the AWS Well-Architected Framework.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning - AI

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). The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3.

AWS 99
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Using Amazon Q Business with AWS HealthScribe to gain insights from patient consultations

AWS Machine Learning - AI

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.

AWS 93
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Efficiently train models with large sequence lengths using Amazon SageMaker model parallel

AWS Machine Learning - AI

Across diverse industries—including healthcare, finance, and marketing—organizations are now engaged in pre-training and fine-tuning these increasingly larger LLMs, which often boast billions of parameters and larger input sequence length. This approach reduces memory pressure and enables efficient training of large models.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning - AI

With the QnABot on AWS (QnABot), integrated with Microsoft Azure Entra ID access controls, Principal launched an intelligent self-service solution rooted in generative AI. Principal also used the AWS open source repository Lex Web UI to build a frontend chat interface with Principal branding.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

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.