Flipboard

OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. Let’s understand how these AWS services are integrated in detail.

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AWS Machine Learning Blog

OCTOBER 24, 2023

In today’s information age, the vast volumes of data housed in countless documents present both a challenge and an opportunity for businesses. Traditional document processing methods often fall short in efficiency and accuracy, leaving room for innovation, cost-efficiency, and optimizations. However, the potential doesn’t end there.

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AWS Machine Learning Blog

JANUARY 8, 2024

AWS customers in healthcare, financial services, the public sector, and other industries store billions of documents as images or PDFs in Amazon Simple Storage Service (Amazon S3). In this post, we focus on processing a large collection of documents into raw text files and storing them in Amazon S3.

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AWS Machine Learning Blog

AUGUST 9, 2024

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Enterprises in industries like manufacturing, finance, and healthcare are inundated with a constant flow of documents—from financial reports and contracts to patient records and supply chain documents. The Amazon S3 upload triggers an AWS Lambda function execution.

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Flipboard

NOVEMBER 27, 2024

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. or a later version) database.

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AWS Machine Learning Blog

MARCH 13, 2024

In this post, we introduce solutions that enable audio and text chat moderation using various AWS services, including Amazon Transcribe , Amazon Comprehend , Amazon Bedrock , and Amazon OpenSearch Service. OpenSearch Service is a fully managed service that makes it straightforward to deploy, scale, and operate OpenSearch in the AWS Cloud.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

AWS can play a key role in enabling fast implementation of these decentralized clinical trials. By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices. According to the EPA, the average passenger vehicle emits about 4.6

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

Legal professionals often spend a significant portion of their work searching through and analyzing large documents to draw insights, prepare arguments, create drafts, and compare documents. AWS AI and machine learning (ML) services help address these concerns within the industry. At AWS, security is our top priority.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

It works by analyzing the visual content to find similar images in its database. Store embeddings : Ingest the generated embeddings into an OpenSearch Serverless vector index, which serves as the vector database for the solution. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3.

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AWS Machine Learning Blog

APRIL 18, 2024

Tools like Terraform and AWS CloudFormation are pivotal for such transitions, offering infrastructure as code (IaC) capabilities that define and manage complex cloud environments with precision. Traditionally, cloud engineers learning IaC would manually sift through documentation and best practices to write compliant IaC scripts.

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AWS Machine Learning Blog

MAY 30, 2024

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. The framework for connecting Anthropic Claude 2 and CBRE’s sample database was implemented using LangChain.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

Reduced operational overhead – The EMR Serverless integration with AWS streamlines big data processing by managing the underlying infrastructure, freeing up your team’s time and resources. Runtime roles are AWS Identity and Access Management (IAM) roles that you can specify when submitting a job or query to an EMR Serverless application.

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ODSC - Open Data Science

OCTOBER 27, 2023

Companies in sectors like healthcare, finance, legal, retail, and manufacturing frequently handle large numbers of documents as part of their day-to-day operations. These documents often contain vital information that drives timely decision-making, essential for ensuring top-tier customer satisfaction, and reduced customer churn.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.

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Flipboard

NOVEMBER 20, 2023

Retrieval Augmented Generation (RAG) allows you to provide a large language model (LLM) with access to data from external knowledge sources such as repositories, databases, and APIs without the need to fine-tune it. When a user asks a question, it searches the vector database and retrieves documents that are most similar to the user’s query.

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AWS Machine Learning Blog

AUGUST 26, 2024

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. It will be able to answer questions, generate content, and facilitate bidirectional interactions, all while continuously using internal AWS and external data to deliver timely, personalized insights.

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Flipboard

NOVEMBER 27, 2024

Customers want to search through all of the data and applications across their organization, and they want to see the provenance information for all of the documents retrieved. A state machine in AWS Step Functions defines the workflow of the ingestion process by invoking AWS Lambda functions, as illustrated in the following figure.

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AWS Machine Learning Blog

NOVEMBER 20, 2024

Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. The solution combines data from an Amazon Aurora MySQL-Compatible Edition database and data stored in an Amazon Simple Storage Service (Amazon S3) bucket.

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AWS Machine Learning Blog

SEPTEMBER 13, 2023

Searching for insights in a repository of free-form text documents can be like finding a needle in a haystack. A traditional approach might be to use word counting or other basic analysis to parse documents, but with the power of Amazon AI and machine learning (ML) tools, we can gather deeper understanding of the content.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. Alternatively, you can use Amazon DynamoDB , a serverless, fully managed NoSQL database, to store your prompts.

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Flipboard

NOVEMBER 27, 2024

In this post, we show you how to integrate the popular Slack messaging service with AWS generative AI services to build a natural language assistant where business users can ask questions of an unstructured dataset. In this example, we ingest the documentation of the Amazon Well-Architected Framework into the knowledge base.

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Data Science Blog

SEPTEMBER 19, 2023

Documentation and Disaster Recovery Made Easy Data is the lifeblood of any organization, and losing it can be catastrophic. The following Terraform script will create an Azure Resource Group, a SQL Server, and a SQL Database. So why using IaC for Cloud Data Infrastructures?

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Implementing a multi-modal agent with AWS consolidates key insights from diverse structured and unstructured data on a large scale. All this is achieved using AWS services, thereby increasing the financial analyst’s efficiency to analyze multi-modal financial data (text, speech, and tabular data) holistically.

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AWS Machine Learning Blog

MARCH 14, 2024

Generative AI with AWS The emergence of FMs is creating both opportunities and challenges for organizations looking to use these technologies. You can use AWS PrivateLink with Amazon Bedrock to establish private connectivity between your FMs and your VPC without exposing your traffic to the internet.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

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.

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Flipboard

NOVEMBER 17, 2023

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. Set up the database access and network access. As always, AWS welcomes feedback.

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AWS Machine Learning Blog

MARCH 10, 2023

This approach allows you to react to the potentially fraudulent transactions in real time as you store each transaction in a database and inspect it before processing further. During claims processing, you collect all the claims documents and then run them through a fraud detection system. An example use case is claims processing.

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Flipboard

NOVEMBER 19, 2024

A common adoption pattern is to introduce document search tools to internal teams, especially advanced document searches based on semantic search. In a real-world scenario, organizations want to make sure their users access only documents they are entitled to access. The following diagram depicts the solution architecture.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

Managing cloud costs and understanding resource usage can be a daunting task, especially for organizations with complex AWS deployments. AWS Cost and Usage Reports (AWS CUR) provides valuable data insights, but interpreting and querying the raw data can be challenging.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Access to car manuals and technical documentation helps the agent provide additional context for curated guidance, enhancing the quality of customer interactions. The workflow includes the following steps: Documents (owner manuals) are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.

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AWS Machine Learning Blog

MAY 30, 2024

To serve their customers, Vitech maintains a repository of information that includes product documentation (user guides, standard operating procedures, runbooks), which is currently scattered across multiple internal platforms (for example, Confluence sites and SharePoint folders).

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Flipboard

NOVEMBER 19, 2024

Amazon Bedrock offers a serverless experience so you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using AWS tools without having to manage infrastructure. AI-powered email processing engine – Central to the solution, this engine uses AI to analyze and process emails.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Instead of relying solely on their pre-trained knowledge, RAG allows models to pull data from documents, databases, and more. This means that as new data becomes available, it can be added to the retrieval database without needing to retrain the entire model. The AWS Command Line Interface (AWS CLI) v2. Python 3.6

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AWS Machine Learning Blog

APRIL 8, 2024

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. However, in many situations, you may need to retrieve documents created in a defined period or tagged with certain categories. This way, you have control over the retrieved documents, especially if your queries are ambiguous.

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AWS Machine Learning Blog

OCTOBER 14, 2024

For many of these use cases, businesses are building Retrieval Augmented Generation (RAG) style chat-based assistants, where a powerful LLM can reference company-specific documents to answer questions relevant to a particular business or use case. Generate a grounded response to the original question based on the retrieved documents.

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AWS Machine Learning Blog

AUGUST 7, 2024

It works by first retrieving relevant responses from a database, then using those responses as context to feed the generative model to produce a final output. For example, retrieving responses from its database before generating a response could provide more relevant and coherent responses. join(batch_text_arr) s3.put_object(

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