Flipboard

APRIL 23, 2025

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.

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AWS Machine Learning Blog

NOVEMBER 17, 2023

You can deploy this solution to your AWS account using the AWS Cloud Development Kit (AWS CDK) package available in our GitHub repo. This process involves the utilization of both ML and non-ML algorithms. Using the AWS Management Console , you can create a recording configuration and link it to an Amazon IVS channel.

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AWS Machine Learning Blog

MAY 23, 2023

In this post, we start with an overview of MLOps and its benefits, describe a solution to simplify its implementations, and provide details on the architecture. We finish with a case study highlighting the benefits realize by a large AWS and PwC customer who implemented this solution. The following diagram illustrates the workflow.

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Towards AI

MARCH 1, 2023

The strategic partnership between Hugging Face and Amazon Web Services (AWS) looks like a positive step in this direction and should increase the availability of open-source data sets and models hosted on Hugging Face. We were also pleased to see the release of Meta’s LLaMA, 4 foundation models ranging from 7B to 65B parameters.

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The MLOps Blog

MARCH 12, 2024

Optimization: Use database optimizations like approximate nearest neighbor ( ANN ) search algorithms to balance speed and accuracy in retrieval tasks. Combine this with the serverless BentoCloud or an auto-scaling group on a cloud platform like AWS to ensure your resources match the demand. Caption : RAG system architecture.

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AWS Machine Learning Blog

AUGUST 15, 2023

System complexity – The architecture complexity requires investments in MLOps to ensure the ML inference process scales efficiently to meet the growing content submission traffic. With the high accuracy of Amazon Rekognition, the team has been able to automate more decisions, save costs, and simplify their system architecture.

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