Category: Industries

In this post, we discuss how FMs can reliably automate the classification of insurance service emails through prompt engineering. When formulating the problem as a classification task, an FM can perform well enough for production environments, while maintaining extensibility into other tasks and getting up and running quickly. All experiments were conducted using Anthropic’s Claude models on Amazon Bedrock.

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment. Through practical examples, we show you how to adapt this FM to these specific use cases while optimizing computational resources.

In this post, we show you how Amazon Web Services (AWS) helps in solving forecasting challenges by customizing machine learning (ML) models for forecasting. We dive into Amazon SageMaker Canvas and explain how SageMaker Canvas can solve forecasting challenges for retail and consumer packaged goods (CPG) enterprises.

This post introduces HCLTech’s AutoWise Companion, a transformative generative AI solution designed to enhance customers’ vehicle purchasing journey. In this post, we analyze the current industry challenges and guide readers through the AutoWise Companion solution functional flow and architecture design using built-in AWS services and open source tools. Additionally, we discuss the design from security and responsible AI perspectives, demonstrating how you can apply this solution to a wider range of industry scenarios.

In this post, we show how you can use our enterprise graph machine learning (GML) framework GraphStorm to solve prediction challenges on large-scale complex networks inspired by our practices of exploring GML to mitigate the AWS backbone network congestion risk.

In this blog post, we explore a client services agent assistant application developed by the London Stock Exchange Group (LSEG) using Amazon Q Business. We will discuss how Amazon Q Business saved time in generating answers, including summarizing documents, retrieving answers to complex Member enquiries, and combining information from different data sources (while providing in-text citations to the data sources used for each answer).

TUI Group is one of the world’s leading global tourism services, providing 21 million customers with an unmatched holiday experience in 180 regions. The TUI content teams are tasked with producing high-quality content for its websites, including product details, hotel information, and travel guides, often using descriptions written by hotel and third-party partners. In this post, we discuss how we used Amazon SageMaker and Amazon Bedrock to build a content generator that rewrites marketing content following specific brand and style guidelines.

In this post, we explore Clearwater Analytics’ foray into generative AI, how they’ve architected their solution with Amazon SageMaker, and dive deep into how Clearwater Analytics is using LLMs to take advantage of more than 18 years of experience within the investment management domain while optimizing model cost and performance.

n this post, we create a virtual analyst that can answer natural language queries of stocks matching certain technical indicator criteria using Amazon Bedrock Agents.

This post explores how 123RF used Amazon Bedrock, Anthropic’s Claude 3 Haiku, and a vector store to efficiently translate content metadata, significantly reduce costs, and improve their global content discovery capabilities.