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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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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning Blog

In this post, you will learn how Marubeni is optimizing market decisions by using the broad set of AWS analytics and ML services, to build a robust and cost-effective Power Bid Optimization solution. AWS Step Functions to orchestrate both the data and ML pipelines. One function to consolidate and prepare the data for training.

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

AWS Machine Learning Blog

We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. The SQL is run by Amazon Athena to return the relevant data. Our final solution is a combination of these text-to-SQL and text-RAG approaches. The following table contains some example responses.

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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. Analysts need to learn new tools and even some programming languages such as SQL (with different variations). Delete the S3 buckets created by AWS CloudFormation and then delete the CloudFormation stack.

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How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)

IBM Journey to AI blog

This data will be analyzed using Netezza SQL and Python code to determine if the flight delays for the first half of 2022 have increased over flight delays compared to earlier periods of time within the current data (January 2019 – December 2021). Figure 7 – Initial query using the historical data (2003 – 2018).

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$100M+ ARR: Alation Achieves Centaur Status

Alation

Our ability to catalog every data asset means that we can partner with other ISVs in data quality and observability, like BigEye and Soda ; privacy, like BigID and OneTrust; access governance, like Immuta and Privacera; not to mention the core platforms, like Snowflake , Databricks , AWS , GCP, and Azure. Subscribe to Alation's Blog.

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How to Optimize Power BI and Snowflake for Advanced Analytics

phData

Snowflake was originally launched in October 2014, but it wasn’t until 2018 that Snowflake became available on Azure. The June 2021 release of Power BI Desktop introduced Custom SQL queries to Snowflake in DirectQuery mode. In late 2021, Power BI introduced custom SQL queries to Snowflake using DirectQuery.