Defined and implemented initative - ML for Data
Data cataloging and classification: Used AI to automatically catalog and classify data assets - identifying the type of data, its source, and its sensitivity. This can help organizations to better understand their data landscape, improve data governance, and ensure compliance with regulations.
Data quality: Used Machine Learning to identify and correct data quality issues, such as missing values, inconsistencies, and errors. This can help to improve the accuracy and reliability of data for downstream analytics and machine learning applications.
Data lineage: Used ML to track the lineage of data assets, identifying their sources and transformations. This can help organizations to understand the provenance of their data, improve data reliability, and troubleshoot data quality issues.
Data governance: Used Machine Learning to automate data governance tasks, such as access control, data retention, and data privacy. This can help organizations to reduce the operational burden of data governance and improve compliance.
Gold Dataset: Build Gold Dataset to build data for AI. This includes weak supervision, Active Learning, optimal transport, and Synthetic data generation. Also used ML for automatic data cleaning, feature engineering, identifying feature interaction, and feature cross.