[Video tutorial] Work With Encrypted DataFrames Using Concrete ML

Concrete ML is a set of privacy-preserving machine learning tools designed to simplify the application of Fully Homomorphic Encryption (FHE) by enabling developers to automatically convert machine learning models into their homomorphic equivalents. In a tutorial by Zama team member Roman Bredehoft, users are guided on how to work with encrypted DataFrames using Concrete ML, which underscores the tool's practicality in preserving data privacy. The initiative encourages engagement through multiple channels, including starring their GitHub repository, exploring comprehensive documentation, joining community discussions, contributing via the Zama Bounty Program, and participating in a developer survey to further advance the FHE space.