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Accelerate custom labeling workflows in Amazon SageMaker Ground Truth without using AWS Lambda

AWS Machine Learning Blog

Previously, setting up a custom labeling job required specifying two AWS Lambda functions: a pre-annotation function, which is run on each dataset object before it’s sent to workers, and a post-annotation function, which is run on the annotations of each dataset object and consolidates multiple worker annotations if needed.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

Yes, the AWS re:Invent season is upon us and as always, the place to be is Las Vegas! are the sessions dedicated to AWS DeepRacer ! Generative AI is at the heart of the AWS Village this year. You marked your calendars, you booked your hotel, and you even purchased the airfare. And last but not least (and always fun!)

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Brilliant words, brilliant writing: Using AWS AI chips to quickly deploy Meta LLama 3-powered applications

AWS Machine Learning Blog

However, customers who want to deploy LLMs in their own self-managed workflows for greater control and flexibility of underlying resources can use these LLMs optimized on top of AWS Inferentia2-powered Amazon Elastic Compute Cloud (Amazon EC2) Inf2 instances. model, but the same process can be followed for the Mistral-7B-instruct-v0.3

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Deploy pre-trained models on AWS Wavelength with 5G edge using Amazon SageMaker JumpStart

AWS Machine Learning Blog

Retailers can deliver more frictionless experiences on the go with natural language processing (NLP), real-time recommendation systems, and fraud detection. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

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Improve LLM application robustness with Amazon Bedrock Guardrails and Amazon Bedrock Agents

AWS Machine Learning Blog

These agents work with AWS managed infrastructure capabilities and Amazon Bedrock , reducing infrastructure management overhead. Prerequisites To run this demo in your AWS account, complete the following prerequisites: Create an AWS account if you don’t already have one. For more details, see Amazon S3 pricing.

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Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2

AWS Machine Learning Blog

In this post, we show how you can run Stable Diffusion models and achieve high performance at the lowest cost in Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EC2 Inf2 instances powered by AWS Inferentia2. versions on AWS Inferentia2 cost-effectively. You can run both Stable Diffusion 2.1 The Stable Diffusion 2.1

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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning Blog

The proposed solution in this post uses fine-tuning of pre-trained large language models (LLMs) to help generate summarizations based on findings in radiology reports. This post demonstrates a strategy for fine-tuning publicly available LLMs for the task of radiology report summarization using AWS services.

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