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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

Introducing Amazon SageMaker partner AI apps Today, we’re excited to announce that AI apps from AWS Partners are now available in SageMaker. SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party.

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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning Blog

This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution.

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AWS performs fine-tuning on a Large Language Model (LLM) to classify toxic speech for a large gaming company

AWS Machine Learning Blog

In an effort to create and maintain a socially responsible gaming environment, AWS Professional Services was asked to build a mechanism that detects inappropriate language (toxic speech) within online gaming player interactions. Unfortunately, as in the real world, not all players communicate appropriately and respectfully.

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Deploy large language models for a healthtech use case on Amazon SageMaker

AWS Machine Learning Blog

In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried. We implemented the solution using the AWS Cloud Development Kit (AWS CDK).

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

Because they’re in a highly regulated domain, HCLS partners and customers seek privacy-preserving mechanisms to manage and analyze large-scale, distributed, and sensitive data. To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data.

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