Snowflake inc. (20240273417). MULTI-PARTY MACHINE LEARNING USING A DATABASE CLEANROOM simplified abstract
Contents
MULTI-PARTY MACHINE LEARNING USING A DATABASE CLEANROOM
Organization Name
Inventor(s)
Orestis Kostakis of Redmond WA (US)
Justin Langseth of Kailua HI (US)
MULTI-PARTY MACHINE LEARNING USING A DATABASE CLEANROOM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240273417 titled 'MULTI-PARTY MACHINE LEARNING USING A DATABASE CLEANROOM
The abstract describes a data sharing system implemented as a local application in a consumer database of a distributed database. The system includes a training function and a scoring function to train a machine learning model on provider and consumer data, generating output data by applying the trained model on input data, which consists of data portions from both consumer and provider databases joined to create a joined dataset for scoring.
- The system is implemented as a local application in a consumer database of a distributed database.
- It includes a training function and a scoring function for machine learning model training and data output generation.
- Input data is created by joining data portions from consumer and provider databases to form a joined dataset for scoring.
Potential Applications: - Data sharing and analysis in consumer databases - Machine learning model training and scoring in a distributed database environment
Problems Solved: - Efficient data sharing and analysis between providers and consumers - Automated machine learning model training and scoring process
Benefits: - Improved data analysis capabilities - Streamlined machine learning model training process - Enhanced collaboration between data providers and consumers
Commercial Applications: Title: Enhanced Data Sharing and Analysis System for Consumer Databases Description: This technology can be used in various industries such as healthcare, finance, and marketing for efficient data sharing and analysis, leading to improved decision-making processes and insights.
Questions about the technology: 1. How does this system improve data sharing between providers and consumers? - This system streamlines the process by training machine learning models on provider and consumer data, generating output data for analysis. 2. What are the potential applications of this technology in different industries? - This technology can be applied in healthcare, finance, and marketing for data analysis and decision-making processes.
Original Abstract Submitted
embodiments of the present disclosure may provide a data sharing system implemented as a local application in a consumer database of a distributed database. the local application can include a training function and a scoring function to train a machine learning model on provider and consumer data, and generate output data by applying the trained machine learning model on input data. the input data can include data portions from a consumer database and a provider database that are joined to create a joined dataset for scoring.
