AI/ML

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Build a domain‐aware data preprocessing pipeline: A multi‐agent collaboration approach

Advanced (300), AI/ML, Amazon Bedrock, Amazon Bedrock Agents, Generative AI, Technical How-to /

Enterprises—especially in the insurance industry—face increasing challenges in processing vast amounts of unstructured data from diverse formats, including PDFs, spreadsheets, images, videos, and audio files. These might include claims document packages, crash event videos, chat transcripts, or policy documents. All contain critical information across the claims processing lifecycle. Traditional data preprocessing methods, though functional, might […]

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Automating complex document processing: How Onity Group built an intelligent solution using Amazon Bedrock

AI/ML, Amazon Bedrock, Amazon Bedrock Agents, Amazon Textract, Artificial Intelligence, AWS Customer, Customer Solutions, Generative AI, Generative BI /

In the mortgage servicing industry, efficient document processing can mean the difference between business growth and missed opportunities. This post explores how Onity Group, a financial services company specializing in mortgage servicing and origination, used Amazon Bedrock and other AWS services to transform their document processing capabilities. Onity Group, founded in 1988, is headquartered in

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Cost-effective AI image generation with PixArt-Σ inference on AWS Trainium and AWS Inferentia

AI/ML, AWS Inferentia, AWS Neuron, AWS Trainium, Generative AI, Neuron, Technical How-to /

PixArt-Sigma is a diffusion transformer model that is capable of image generation at 4k resolution. This model shows significant improvements over previous generation PixArt models like Pixart-Alpha and other diffusion models through dataset and architectural improvements. AWS Trainium and AWS Inferentia are purpose-built AI chips to accelerate machine learning (ML) workloads, making them ideal for

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How Hexagon built an AI assistant using AWS generative AI services

AI/ML, Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon Elastic Kubernetes Service, Customer Solutions, Generative AI /

This post was co-written with Julio P. Roque Hexagon ALI. Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding these advantages, we partnered with AWS to embark on a journey to develop HxGN Alix,

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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

Advanced (300), AI/ML, AIML, Amazon Bedrock, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker Data & AI Governance, Generative AI, Responsible AI /

Generative AI is rapidly reshaping industries worldwide, empowering businesses to deliver exceptional customer experiences, streamline processes, and push innovation at an unprecedented scale. However, amidst the excitement, critical questions around the responsible use and implementation of such powerful technology have started to emerge. Although responsible AI has been a key focus for the industry over

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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

AI/ML, Amazon Machine Learning, Amazon SageMaker, Amazon SageMaker AI, Artificial Intelligence, Customer Solutions, Generative AI, Natural Language Processing, Technical How-to /

Archival data in research institutions and national laboratories represents a vast repository of historical knowledge, yet much of it remains inaccessible due to factors like limited metadata and inconsistent labeling. Traditional keyword-based search mechanisms are often insufficient for locating relevant documents efficiently, requiring extensive manual review to extract meaningful insights. To address these challenges, a

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Optimizing Mixtral 8x7B on Amazon SageMaker with AWS Inferentia2

AI/ML, Amazon EC2, Amazon SageMaker, AWS Inferentia, AWS Neuron, Generative AI, Technical How-to /

Organizations are constantly seeking ways to harness the power of advanced large language models (LLMs) to enable a wide range of applications such as text generation, summarizationquestion answering, and many others. As these models grow more powerful and capable, deploying them in production environments while optimizing performance and cost-efficiency becomes more challenging. Amazon Web Services

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Build multi-agent systems with LangGraph and Amazon Bedrock

Advanced (300), AI/ML, Amazon Bedrock, Artificial Intelligence, Generative AI, Technical How-to /

Large language models (LLMs) have raised the bar for human-computer interaction where the expectation from users is that they can communicate with their applications through natural language. Beyond simple language understanding, real-world applications require managing complex workflows, connecting to external data, and coordinating multiple AI capabilities. Imagine scheduling a doctor’s appointment where an AI agent

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Building an AIOps chatbot with Amazon Q Business custom plugins

AI/ML, Amazon Q Business, Technical How-to /

Many organizations rely on multiple third-party applications and services for different aspects of their operations, such as scheduling, HR management, financial data, customer relationship management (CRM) systems, and more. However, these systems often exist in silos, requiring users to manually navigate different interfaces, switch between environments, and perform repetitive tasks, which can be time-consuming and

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Automating regulatory compliance: A multi-agent solution using Amazon Bedrock and CrewAI

AI/ML, Amazon Bedrock, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Machine Learning, cybersecurity, Generative AI, Intermediate (200) /

Financial institutions today face an increasingly complex regulatory world that demands robust, efficient compliance mechanisms. Although organizations traditionally invest countless hours reviewing regulations such as the Anti-Money Laundering (AML) rules and the Bank Secrecy Act (BSA), modern AI solutions offer a transformative approach to this challenge. By using Amazon Bedrock Knowledge Bases alongside CrewAI—an open

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