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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

ML 100
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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.

AWS 111
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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning Blog

The failed instance also needs to be isolated and terminated manually, either through the AWS Management Console , AWS Command Line Interface (AWS CLI), or tools like kubectl or eksctl. Frontier model builders can further enhance model performance using built-in ML tools within SageMaker HyperPod.

ML 114
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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

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Solution overview The NER & LLM Gen AI Application is a document processing solution built on AWS that combines NER and LLMs to automate document analysis at scale. The system then orchestrates the creation of necessary model endpoints, processes documents in batches for efficiency, and automatically cleans up resources upon completion.

AWS 110
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Create a multimodal chatbot tailored to your unique dataset with Amazon Bedrock FMs

AWS Machine Learning Blog

In this post, we show how to create a multimodal chat assistant on Amazon Web Services (AWS) using Amazon Bedrock models, where users can submit images and questions, and text responses will be sourced from a closed set of proprietary documents. For this post, we recommend activating these models in the us-east-1 or us-west-2 AWS Region.

AWS 127
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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

Challenges in deploying advanced ML models in healthcare Rad AI, being an AI-first company, integrates machine learning (ML) models across various functions—from product development to customer success, from novel research to internal applications. Rad AI’s ML organization tackles this challenge on two fronts.

ML 112
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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

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Throughout these examples, you will learn how the comprehensive suite of AWS services, including Amazon Bedrock and Amazon SageMaker , are the key to success. The quality assurance process includes automated testing methods combining ML-, algorithm-, or LLM-based evaluations. The team extensively used fine-tuned SLMs.

AI 158