AWS Machine Learning Blog

JANUARY 7, 2025

The solution proposed in this post relies on LLMs context learning capabilities and prompt engineering. It enables you to use an off-the-shelf model as is without involving machine learning operations (MLOps) activity. The solution offers two TM retrieval modes for users to choose from: vector and document search.

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MARCH 21, 2025

AI for IT operations (AIOps) is the application of AI and machine learning (ML) technologies to automate and enhance IT operations. This post presents a comprehensive AIOps solution that combines various AWS services such as Amazon Bedrock , AWS Lambda , and Amazon CloudWatch to create an AI assistant for effective incident management.

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AWS Machine Learning Blog

DECEMBER 11, 2024

Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI. Field Advisor serves four primary use cases: AWS-specific knowledge search With Amazon Q Business, weve made internal data sources as well as public AWS content available in Field Advisors index.

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AWS Machine Learning Blog

DECEMBER 12, 2024

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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AWS Machine Learning Blog

NOVEMBER 13, 2024

Exclusive to Amazon Bedrock, the Amazon Titan family of models incorporates 25 years of experience innovating with AI and machine learning at Amazon. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3. If enabled, its status will display as Access granted.

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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

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

APRIL 3, 2025

Every year, AWS Sales personnel draft in-depth, forward looking strategy documents for established AWS customers. These documents help the AWS Sales team to align with our customer growth strategy and to collaborate with the entire sales team on long-term growth ideas for AWS customers.

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AWS Machine Learning Blog

MARCH 10, 2025

The traditional approach of manually sifting through countless research documents, industry reports, and financial statements is not only time-consuming but can also lead to missed opportunities and incomplete analysis. Along the way, it also simplified operations as Octus is an AWS shop more generally.

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JANUARY 6, 2025

(Precise), an Amazon Web Services (AWS) Partner , participated in the AWS Think Big for Small Business Program (TBSB) to expand their AWS capabilities and to grow their business in the public sector. The platform helped the agency digitize and process forms, pictures, and other documents.

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AWS Machine Learning Blog

OCTOBER 31, 2024

AWS offers powerful generative AI services , including Amazon Bedrock , which allows organizations to create tailored use cases such as AI chat-based assistants that give answers based on knowledge contained in the customers’ documents, and much more. The following figure illustrates the high-level design of the solution.

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AWS Machine Learning Blog

NOVEMBER 25, 2024

You can try out the models with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. The model is deployed in an AWS secure environment and under your virtual private cloud (VPC) controls, helping provide data security.

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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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NOVEMBER 27, 2024

Within seconds of transactional data being written into Amazon Aurora (a fully managed modern relational database service offering performance and high availability at scale), the data is seamlessly made available in Amazon Redshift for analytics and machine learning. Choose the us-east-1 AWS Region in which to create the stack.

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AWS Machine Learning Blog

OCTOBER 10, 2024

Amazon Lookout for Vision , the AWS service designed to create customized artificial intelligence and machine learning (AI/ML) computer vision models for automated quality inspection, will be discontinuing on October 31, 2025. For an out-of-the-box solution, the AWS Partner Network offers solutions from multiple partners.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

At AWS, were using the power of models in Amazon Bedrock to drive automation of complex processes that have traditionally been challenging to streamline. In this post, we focus on one such complex workflow: document processing. The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API.

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AWS Machine Learning Blog

MARCH 21, 2025

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.

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AWS Machine Learning Blog

FEBRUARY 6, 2025

Building upon a previous Machine Learning Blog post to create personalized avatars by fine-tuning and hosting the Stable Diffusion 2.1 We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered Amazon EC2 Inf2 instances , unlocking superior price performance for your inference workloads.

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NOVEMBER 20, 2024

By narrowing down the search space to the most relevant documents or chunks, metadata filtering reduces noise and irrelevant information, enabling the LLM to focus on the most relevant content. This approach narrows down the search space to the most relevant documents or passages, reducing noise and irrelevant information.

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AWS Machine Learning Blog

MARCH 21, 2025

We also configure an application environment and grant access to users to interact with an application environment using AWS IAM Identity Center for user management. Weve provided an AWS CloudFormation template in our GitHub repository that you can use to set up the environment for this demonstration.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This solution uses decorators in your application code to capture and log metadata such as input prompts, output results, run time, and custom metadata, offering enhanced security, ease of use, flexibility, and integration with native AWS services.

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AWS Machine Learning Blog

NOVEMBER 22, 2024

This post discusses how to use AWS Step Functions to efficiently coordinate multi-step generative AI workflows, such as parallelizing API calls to Amazon Bedrock to quickly gather answers to lists of submitted questions. sync) pattern, which automatically waits for the completion of asynchronous jobs.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Solution overview This solution uses the Amazon Bedrock Knowledge Bases chat with document feature to analyze and extract key details from your invoices, without needing a knowledge base. Importantly, your document and data are not stored after processing. Make sure your AWS credentials are configured correctly.

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AWS Machine Learning Blog

DECEMBER 23, 2024

Lettria , an AWS Partner, demonstrated that integrating graph-based structures into RAG workflows improves answer precision by up to 35% compared to vector-only retrieval methods. In this post, we explore why GraphRAG is more comprehensive and explainable than vector RAG alone, and how you can use this approach using AWS services and Lettria.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

A key part of the submission process is authoring regulatory documents like the Common Technical Document (CTD), a comprehensive standard formatted document for submitting applications, amendments, supplements, and reports to the FDA. The tedious process of compiling hundreds of documents is also prone to errors.

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AWS Machine Learning Blog

JUNE 12, 2024

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.

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AWS Machine Learning Blog

JUNE 11, 2024

Starting with the AWS Neuron 2.18 release , you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. AWS Systems Manager Parameter Store support Neuron 2.18 and TensorFlow 2.10

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AWS Machine Learning Blog

MARCH 18, 2025

With this launch, you can now deploy NVIDIAs optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. As part of NVIDIA AI Enterprise available in AWS Marketplace , NIM is a set of user-friendly microservices designed to streamline and accelerate the deployment of generative AI.

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AWS Machine Learning Blog

MAY 1, 2024

This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. Virginia) AWS Region. The following diagram illustrates the solution architecture.

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FEBRUARY 5, 2025

Clinical trials involve the ingestion and processing of vast amounts of highly regulated data, including complex protocol documents that describe how

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AWS Machine Learning Blog

OCTOBER 29, 2024

Refer to Supported Regions and models for batch inference for current supporting AWS Regions and models. To address this consideration and enhance your use of batch inference, we’ve developed a scalable solution using AWS Lambda and Amazon DynamoDB. Amazon S3 invokes the {stack_name}-create-batch-queue-{AWS-Region} Lambda function.

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AWS Machine Learning Blog

APRIL 11, 2024

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.

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AWS Machine Learning Blog

MARCH 17, 2025

AWS Lambda orchestrator, along with tool configuration and prompts, handles orchestration and invokes the Mistral model on Amazon Bedrock. Data is stored in a conversation history, and a member database (MemberDB) is used to store member information and the knowledge base has static documents used by the agent.

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AWS Machine Learning Blog

NOVEMBER 6, 2024

Prerequisites Before you dive into the integration process, make sure you have the following prerequisites in place: AWS account – You’ll need an AWS account to access and use Amazon Bedrock. You can interact with Amazon Bedrock using AWS SDKs available in Python, Java, Node.js, and more.

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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. Complete the following steps: Choose an AWS Region Amazon Q supports (for this post, we use the us-east-1 Region). aligned identity provider (IdP).

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AWS Machine Learning Blog

JANUARY 13, 2025

This is where intelligent document processing (IDP), coupled with the power of generative AI , emerges as a game-changing solution. The process involves the collection and analysis of extensive documentation, including self-evaluation reports (SERs), supporting evidence, and various media formats from the institutions being reviewed.

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OCTOBER 25, 2023

Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. 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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