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Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

AWS Machine Learning

In this post, we focus on one such complex workflow: document processing. Rule-based systems or specialized machine learning (ML) models often struggle with the variability of real-world documents, especially when dealing with semi-structured and unstructured data.

APIs 86
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Answer questions from tables embedded in documents with Amazon Q Business

AWS Machine Learning

A large portion of that information is found in text narratives stored in various document formats such as PDFs, Word files, and HTML pages. Some information is also stored in tables (such as price or product specification tables) embedded in those same document types, CSVs, or spreadsheets.

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Guest Post: Here Are The Do’s & Don’ts of Social Media Customer Service

ShepHyken

Compile the most frequently asked questions in a shared document, determine the best possible answers, and distribute the document to your customer service team. . This document will act as a single source of truth your team can reference. Don’t engage with spam accounts . 4 Don’ts of Social Media Customer Service .

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Scalable intelligent document processing using Amazon Bedrock

AWS Machine Learning

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. The Anthropic Claude 3 Haiku model then processes the documents and returns the desired information, streamlining the entire workflow.

APIs 129
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Customer Success Needs to Grow (Up)

Speaker: Peter Armaly - Senior Director and Advisor of Customer Success at Oracle

Customer success is a well-established practice in the enterprise business world (70% of companies have a dedicated team, according to TSIA) and the benefits it delivers to customers are real and well-documented. Demand more accountability of the practice. With revenue, comes accountability. It’s time to go the distance.

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Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model

AWS Machine Learning

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Categorizing documents is an important first step in IDP systems.

APIs 129
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Customize Amazon Textract with business-specific documents using Custom Queries

AWS Machine Learning

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents. Queries is a feature that enables you to extract specific pieces of information from varying, complex documents using natural language. personal or cashier’s checks), financial institution and country (e.g.,

APIs 127