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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. The sessions showcase how Amazon Q can help you streamline coding, testing, and troubleshooting, as well as enable you to make the most of your data to optimize business operations.

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

AWS Machine Learning Blog

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. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler. Within the data flow, add an Amazon S3 destination node.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

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By narrowing down the search space to the most relevant documents or chunks, metadata filtering reduces noise and irrelevant information, enabling the LLM to focus on the most relevant content. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries.

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A secure approach to generative AI with AWS

AWS Machine Learning Blog

They’re often used with highly sensitive business data, like personal data, compliance data, operational data, and financial information, to optimize the model’s output. At AWS, our top priority is safeguarding the security and confidentiality of our customers’ workloads.

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Evaluate healthcare generative AI applications using LLM-as-a-judge on AWS

AWS Machine Learning Blog

Through real-time retrieval of relevant medical information, RAG systems can provide more reliable and contextually appropriate responses, making them particularly valuable for healthcare applications where precision is crucial. The reports were de-identified using a rule-based approach to remove protected health information.

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Migrate Amazon SageMaker Data Wrangler flows to Amazon SageMaker Canvas for faster data preparation

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler provides a visual interface to streamline and accelerate data preparation for machine learning (ML), which is often the most time-consuming and tedious task in ML projects. About the Authors Charles Laughlin is a Principal AI Specialist at Amazon Web Services (AWS).