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

AWS Machine Learning Blog

However, companies are discovering that performing full fine tuning for these models with their data isnt cost effective. To reduce costs while continuing to use the power of AI , many companies have shifted to fine tuning LLMs on their domain-specific data using Parameter-Efficient Fine Tuning (PEFT).

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A secure approach to generative AI with AWS

AWS Machine Learning Blog

Generative artificial intelligence (AI) is transforming the customer experience in industries across the globe. 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.

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Using responsible AI principles with Amazon Bedrock Batch Inference

AWS Machine Learning Blog

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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

AWS Machine Learning Blog

In our previous blog posts, we explored various techniques such as fine-tuning large language models (LLMs), prompt engineering, and Retrieval Augmented Generation (RAG) using Amazon Bedrock to generate impressions from the findings section in radiology reports using generative AI. Part 1 focused on model fine-tuning.

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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

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Retrieval Augmented Generation (RAG) has become a crucial technique for improving the accuracy and relevance of AI-generated responses. Prerequisites Before proceeding with this tutorial, make sure you have the following in place: AWS account – You should have an AWS account with access to Amazon Bedrock.

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Analyze security findings faster with no-code data preparation using generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared. Prerequisites Before starting, you need an AWS account.