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Accelerating ML experimentation with enhanced security: AWS PrivateLink support for Amazon SageMaker with MLflow

AWS Machine Learning Blog

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

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

AWS Machine Learning Blog

To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023. In this blog post, we showcase how you can perform efficient supervised fine tuning for a Meta Llama 3 model using PEFT on AWS Trainium with SageMaker HyperPod. architectures/5.sagemaker-hyperpod/LifecycleScripts/base-config/

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.

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Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

AWS Machine Learning Blog

Solution overview Our solution uses the AWS integrated ecosystem to create an efficient scalable pipeline for digital pathology AI workflows. Prerequisites We assume you have access to and are authenticated in an AWS account. The AWS CloudFormation template for this solution uses t3.medium

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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Enhancing AWS Support Engineering efficiency The AWS Support Engineering team faced the daunting task of manually sifting through numerous tools, internal sources, and AWS public documentation to find solutions for customer inquiries. Then we introduce the solution deployment using three AWS CloudFormation templates.

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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Accelerate edge AI development with SiMa.ai Edgematic with a seamless AWS integration

AWS Machine Learning Blog

This post is co-authored by Manuel Lopez Roldan, SiMa.ai, and Jason Westra, AWS Senior Solutions Architect. Are you looking to deploy machine learning (ML) models at the edge? Edgematic with SageMaker JupyterLab to deploy an ML model, YOLOv7 , to the edge. If you dont have an AWS account, you can create one.

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