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

AWS Machine Learning Blog

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. It involves training a global machine learning (ML) model from distributed health data held locally at different sites.

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Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning Blog

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

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Implement smart document search index with Amazon Textract and Amazon OpenSearch

AWS Machine Learning Blog

We’ll cover how technologies such as Amazon Textract, AWS Lambda , Amazon Simple Storage Service (Amazon S3), and Amazon OpenSearch Service can be integrated into a workflow that seamlessly processes documents. The main concepts used are the AWS Cloud Development Kit (CDK) constructs, the actual CDK stacks and AWS Step Functions.

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Understanding and predicting urban heat islands at Gramener using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

SageMaker geospatial capabilities make it straightforward for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Among these models, the spatial fixed effect model yielded the highest mean R-squared value, particularly for the timeframe spanning 2014 to 2020.

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Optimize data preparation with new features in AWS SageMaker Data Wrangler

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for machine learning (ML) from weeks to minutes. You can build an ML model with SageMaker Autopilot representing all your data using the manifest file and use that for your ML inference and production deployment.

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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning Blog

GraphStorm is a low-code enterprise graph machine learning (GML) framework to build, train, and deploy graph ML solutions on complex enterprise-scale graphs in days instead of months. introduces refactored graph ML pipeline APIs. in computer systems and architecture at the Fudan University, Shanghai, in 2014. GraphStorm 0.3

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