2018 ML SQL 
AWS Machine Learning Blog
MARCH 14, 2024
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.
AWS Machine Learning Blog
SEPTEMBER 19, 2023
Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. It generates a SQL query using an LLM based on the question and queries the Athena database.
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APRIL 13, 2023
The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.
Tableau
DECEMBER 1, 2021
Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. For example, Tableau’s release v1 (April 2005) connected to structured data in SQL databases (MS Access, MS SQL Server, MySQL) and the two major cube databases (Hyperion Essbase and MS SSAS). March 2021).
AWS Machine Learning Blog
JUNE 26, 2024
This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Analysts need to learn new tools and even some programming languages such as SQL (with different variations). For structured data, the agent uses the SQL Connector and SQLAlchemy to analyze the database through Athena.
NOVEMBER 24, 2023
This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning.
AWS Machine Learning Blog
MARCH 8, 2023
MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. MPII’s bid optimization engine solution uses ML models to generate optimal bids for participation in different markets. Data comes from disparate sources in a number of formats.
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