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Using task-specific models from AI21 Labs on AWS

AWS Machine Learning Blog

In this blog post, we will show you how to leverage AI21 Labs’ Task-Specific Models (TSMs) on AWS to enhance your business operations. You will learn the steps to subscribe to AI21 Labs in the AWS Marketplace, set up a domain in Amazon SageMaker, and utilize AI21 TSMs via SageMaker JumpStart. Limits are account and resource specific.

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Recommend top trending items to your users using the new Amazon Personalize recipe

AWS Machine Learning Blog

Please use below python code to curate interactions dataset from the MovieLens public dataset. Choose the new aws-trending-now recipe. For Solution version ID , choose the solution version that uses the aws-trending-now recipe. For the interactions data, we use ratings history from the movies review dataset, MovieLens.

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Accelerate development of ML workflows with Amazon Q Developer in Amazon SageMaker Studio

AWS Machine Learning Blog

Solution overview If you’re an AWS Identity and Access Management (IAM) and AWS IAM Identity Center user, you can use your Amazon Q Developer Pro tier subscription within Amazon SageMaker. This dataset contains 10 years (1999–2008) of clinical care data at 130 US hospitals and integrated delivery networks.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

Solution overview In the following sections, we provide a step-by-step demonstration for fine-tuning an LLM for text generation tasks via both the JumpStart Studio UI and Python SDK. On August 21, 2009, the Company filed a Form 10-Q for the quarter ended December 31, 2008. per diluted share, compared to $5,716,000, or $0.33

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Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

The first building, which was completed in 2008, is the UP Access Flan-T5 instruction-tuned models in SageMaker JumpStart provides three avenues to get started using these instruction-tuned Flan models: JumpStart foundation models, Studio, and the SageMaker SDK. The CMMH building will be the second building constructed by the UP in the UST.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

Solution overview In the following sections, we provide a step-by-step demonstration for fine-tuning an LLM for text generation tasks via both the JumpStart Studio UI and Python SDK. On August 21, 2009, the Company filed a Form 10-Q for the quarter ended December 31, 2008. per diluted share, compared to $5,716,000, or $0.33

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