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Unstructured data management and governance using AWS AI/ML and analytics services

Flipboard

After decades of digitizing everything in your enterprise, you may have an enormous amount of data, but with dormant value. However, with the help of AI and machine learning (ML), new software tools are now available to unearth the value of unstructured data. The solution integrates data in three tiers.

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How the UNDP Independent Evaluation Office is using AWS AI/ML services to enhance the use of evaluation to support progress toward the Sustainable Development Goals

AWS Machine Learning Blog

In this post, we discuss how the IEO developed UNDP’s artificial intelligence and machine learning (ML) platform—named Artificial Intelligence for Development Analytics (AIDA)— in collaboration with AWS, UNDP’s Information and Technology Management Team (UNDP ITM), and the United Nations International Computing Centre (UNICC).

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Create a multimodal assistant with advanced RAG and Amazon Bedrock

AWS Machine Learning Blog

It combines text, table, and image (including chart) data into a unified vector representation, enabling cross-modal understanding and retrieval. These embeddings represent textual and visual data in a numerical format, which is essential for various natural language processing (NLP) tasks.

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Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines

AWS Machine Learning Blog

MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. MLOps tooling helps you repeatably and reliably build and simplify these processes into a workflow that is tailored for ML. It’s natural to focus on a single model that you want to train and deploy.

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An Overview of the Top Text Annotation Tools For Natural Language Processing

John Snow Labs

Likewise, almost 80% of AI/ML projects stall at some stage before deployment. Developing a machine learning model requires a big amount of training data. Therefore, the data needs to be properly labeled/categorized for a particular use case. Therefore, the data needs to be properly labeled/categorized for a particular use case.

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Information extraction with LLMs using Amazon SageMaker JumpStart

AWS Machine Learning Blog

SageMaker JumpStart is a machine learning (ML) hub with foundation models (FMs), built-in algorithms, and prebuilt ML solutions that you can deploy with just a few clicks. Prompt engineering relies on large pretrained language models that have been trained on massive amounts of text data. .*"

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Amazon Textract’s new Layout feature introduces efficiencies in general purpose and generative AI document processing tasks

AWS Machine Learning Blog

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. AnalyzeDocument Layout is a new feature that allows customers to automatically extract layout elements such as paragraphs, titles, subtitles, headers, footers, and more from documents.