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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive. The Process Data Lambda function redacts sensitive data through Amazon Comprehend.

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Precise Software Solutions implements ML as a service on AWS to save time and money for federal agency

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The platform helped the agency digitize and process forms, pictures, and other documents. Using the platform, which uses Amazon Textract , AWS Fargate , and other services, the agency gained a four-fold productivity improvement by streamlining and automating labor-intensive manual processes.

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Accelerate your ML lifecycle using the new and improved Amazon SageMaker Python SDK – Part 1: ModelTrainer

AWS Machine Learning Blog

The new SDK is designed with a tiered user experience in mind, where the new lower-level SDK ( SageMaker Core ) provides access to full breadth of SageMaker features and configurations, allowing for greater flexibility and control for ML engineers. For the detailed list of pre-set values, refer to the SDK documentation.

ML 80
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Syngenta develops a generative AI assistant to support sales representatives using Amazon Bedrock Agents

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As a global leader in agriculture, Syngenta has led the charge in using data science and machine learning (ML) to elevate customer experiences with an unwavering commitment to innovation. Efficient metadata storage with Amazon DynamoDB – To support quick and efficient data retrieval, document metadata is stored in Amazon DynamoDB.

AWS 148
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Intelligent healthcare assistants: Empowering stakeholders with personalized support and data-driven insights

AWS Machine Learning Blog

Large language models (LLMs) have revolutionized the field of natural language processing, enabling machines to understand and generate human-like text with remarkable accuracy. However, despite their impressive language capabilities, LLMs are inherently limited by the data they were trained on.

AWS 96
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Process formulas and charts with Anthropic’s Claude on Amazon Bedrock

AWS Machine Learning Blog

Research papers and engineering documents often contain a wealth of information in the form of mathematical formulas, charts, and graphs. Navigating these unstructured documents to find relevant information can be a tedious and time-consuming task, especially when dealing with large volumes of data.

AWS 110
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Techniques for automatic summarization of documents using language models

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Tools like LangChain , combined with a large language model (LLM) powered by Amazon Bedrock or Amazon SageMaker JumpStart , simplify the implementation process. Implementation includes the following steps: The first step is to break down the large document, such as a book, into smaller sections, or chunks.

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