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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

Flipboard

In this post, we propose an end-to-end solution using Amazon Q Business to address similar enterprise data challenges, showcasing how it can streamline operations and enhance customer service across various industries. The Process Data Lambda function redacts sensitive data through Amazon Comprehend.

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AI and Blockchain Integration for Preserving Privacy

Unite.AI

NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT. Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more.

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Is There a Library for Cleaning Data before Tokenization? Meet the Unstructured Library for Seamless Pre-Tokenization Cleaning

Marktechpost

In Natural Language Processing (NLP) tasks, data cleaning is an essential step before tokenization, particularly when working with text data that contains unusual word separations such as underscores, slashes, or other symbols in place of spaces. The post Is There a Library for Cleaning Data before Tokenization?

NLP 122
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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

In addition, the Amazon Bedrock Knowledge Bases team worked closely with us to address several critical elements, including expanding embedding limits, managing the metadata limit (250 characters), testing different chunking methods, and syncing throughput to the knowledge base.

DevOps 69
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Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

AWS Machine Learning Blog

In this post, we demonstrate how data aggregated within the AWS CCI Post Call Analytics solution allowed Principal to gain visibility into their contact center interactions, better understand the customer journey, and improve the overall experience between contact channels while also maintaining data integrity and security.

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Accenture creates a Knowledge Assist solution using generative AI services on AWS

AWS Machine Learning Blog

Each request/response interaction is facilitated by the AWS SDK and sends network traffic to Amazon Lex (the NLP component of the bot). Metadata about the request/response pairings are logged to Amazon CloudWatch. The CloudWatch log group is configured with a subscription filter that sends logs into Amazon OpenSearch Service.

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How to Save Trained Model in Python

The MLOps Blog

2 For dynamic models, such as those with variable-length inputs or outputs, which are frequent in natural language processing (NLP) and computer vision, PyTorch offers improved support. Finally, you can store the model and other metadata information using the INSERT INTO command. track dataset version model[ "data/train_and_test" ].track_files(

Python 105