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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate , and Amazon OpenSearch Service. Along the way, it also simplified operations as Octus is an AWS shop more generally.

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Intelligent document processing at scale with generative AI and Amazon Bedrock Data Automation

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Furthermore, classic NER models can’t handle other data types such as numeric scores (such as sentiment) or free-form text (such as summary). Generative AI unlocks these possibilities without costly data annotation or model training, enabling more comprehensive intelligent document processing (IDP).

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Configure fine-grained access to Amazon Bedrock models using Amazon SageMaker Unified Studio

AWS Machine Learning Blog

Launched in 2025, SageMaker Unified Studio is a single data and AI development environment where you can find and access the data in your organization and act on it using the best tools across use cases. The default AmazonSageMakerBedrockModelManagementRole has the AWS policy AmazonDataZoneBedrockModelManagementPolicy attached.

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Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

AWS Machine Learning Blog

Managing access control in enterprise machine learning (ML) environments presents significant challenges, particularly when multiple teams share Amazon SageMaker AI resources within a single Amazon Web Services (AWS) account. Refer to the Operating model whitepaper for best practices on account structure.

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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning Blog

This integration addresses these hurdles by providing data scientists and ML engineers with a comprehensive environment that supports the entire ML lifecycle, from development to deployment at scale. Make sure you have the latest version of the AWS Command Line Interface (AWS CLI).

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Use Amazon Bedrock tooling with Amazon SageMaker JumpStart models

AWS Machine Learning Blog

SageMaker JumpStart has long been the go-to service for developers and data scientists seeking to deploy state-of-the-art generative AI models. To access SageMaker Studio on the AWS Management Console , you need to set up an Amazon SageMaker domain. Currently, he is focused on helping AWS customers adopt Generative AI solutions.

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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning Blog

Prerequisites To use this feature, make sure that you have satisfied the following requirements: An active AWS account. model customization is available in the US West (Oregon) AWS Region. Sovik Kumar Nath is an AI/ML and Generative AI senior solution architect with AWS. Meta Llama 3.2 As of writing this post, Meta Llama 3.2

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