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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning Blog

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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AI-powered assistants for investment research with multi-modal data: An application of Agents for Amazon Bedrock

AWS Machine Learning Blog

This post is a follow-up to Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets. Action groups – Action groups are interfaces that an agent uses to interact with the different underlying components such as APIs and databases.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. In the first post , we described FL concepts and the FedML framework.

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Bundesliga Match Fact Keeper Efficiency: Comparing keepers’ performances objectively using machine learning on AWS

AWS Machine Learning Blog

Not only was he widely considered the top-rated goalkeeper in the league during the 2021/22 season, but he also held that title back in 2018/19 when Eintracht Frankfurt reached the Europa League semifinals. The BMF logic itself (except for the ML model) runs on an AWS Fargate container.

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The journey of PGA TOUR’s generative AI virtual assistant, from concept to development to prototype

AWS Machine Learning Blog

In this post we highlight how the AWS Generative AI Innovation Center collaborated with the AWS Professional Services and PGA TOUR to develop a prototype virtual assistant using Amazon Bedrock that could enable fans to extract information about any event, player, hole or shot level details in a seamless interactive manner.

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Generate a counterfactual analysis of corn response to nitrogen with Amazon SageMaker JumpStart solutions

AWS Machine Learning Blog

Prerequisites You need an AWS account to use this solution. To run this JumpStart 1P Solution and have the infrastructure deployed to your AWS account, you need to create an active Amazon SageMaker Studio instance (refer to Onboard to Amazon SageMaker Domain ).

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Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Cloud-based business intelligence (BI): Cloud-based BI tools enable organizations to access and analyze data from cloud-based sources and on-premises databases. For instance, British Airways faced a fine of £183 million ($230 million) for a GDPR breach in 2018. Non-compliance can result in hefty fines.

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