AWS Machine Learning Blog
Category: Industries
How MSD uses Amazon Bedrock to translate natural language into SQL for complex healthcare databases
MSD, a leading pharmaceutical company, collaborates with AWS to implement a powerful text-to-SQL generative AI solution using Amazon Bedrock and Anthropic’s Claude 3.5 Sonnet model. This approach streamlines data extraction from complex healthcare databases like DE-SynPUF, enabling analysts to generate SQL queries from natural language questions. The solution addresses challenges such as coded columns, non-intuitive names, and ambiguous queries, significantly reducing query time and democratizing data access.
Accelerate your financial statement analysis with Amazon Bedrock and generative AI
In this post, we demonstrate how to deploy a generative AI application that can accelerate your financial statement analysis on AWS.
How Zalando optimized large-scale inference and streamlined ML operations on Amazon SageMaker
This post is cowritten with Mones Raslan, Ravi Sharma and Adele Gouttes from Zalando. Zalando SE is one of Europe’s largest ecommerce fashion retailers with around 50 million active customers. Zalando faces the challenge of regular (weekly or daily) discount steering for more than 1 million products, also referred to as markdown pricing. Markdown pricing is […]
Advance environmental sustainability in clinical trials using AWS
In this post, we discuss how to use AWS to support a decentralized clinical trial across the four main pillars of a decentralized clinical trial (virtual trials, personalized patient engagement, patient-centric trial design, and centralized data management). By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.
Build a video insights and summarization engine using generative AI with Amazon Bedrock
This post presents a solution where you can upload a recording of your meeting (a feature available in most modern digital communication services such as Amazon Chime) to a centralized video insights and summarization engine. This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. The solution notes the logged actions per individual and provides suggested actions for the uploader. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.
Using Amazon Q Business with AWS HealthScribe to gain insights from patient consultations
In this post, we discuss how you can use AWS HealthScribe with Amazon Q Business to create a chatbot to quickly gain insights into patient clinician conversations.
How DPG Media uses Amazon Bedrock and Amazon Transcribe to enhance video metadata with AI-powered pipelines
In this post, we share how DPG Media is introducing AI-powered processes using Amazon Bedrock into its video publication pipelines. This solution is helping accelerate audio metadata extraction, create a more engaging user experience, and save time.
SK Telecom improves telco-specific Q&A by fine-tuning Anthropic’s Claude models in Amazon Bedrock
In this post, we share how SKT customizes Anthropic Claude models for telco-specific Q&A regarding technical telecommunication documents of SKT using Amazon Bedrock.
Automate user on-boarding for financial services with a digital assistant powered by Amazon Bedrock
In this post, we present a solution that harnesses the power of generative AI to streamline the user onboarding process for financial services through a digital assistant.
Implement model-independent safety measures with Amazon Bedrock Guardrails
In this post, we discuss how you can use the ApplyGuardrail API in common generative AI architectures such as third-party or self-hosted large language models (LLMs), or in a self-managed Retrieval Augmented Generation (RAG) architecture.