[Video tutorial] Fine-tune LLM Models on Encrypted Data using Concrete ML

Concrete ML is a set of tools designed to facilitate privacy-preserving machine learning by enabling the automatic conversion of machine learning models into their fully homomorphic encrypted counterparts. In a tutorial by Zama team member Celia Kherfallah, users are guided on building an encrypted DNA testing application using Concrete ML and Fully Homomorphic Encryption (FHE). The initiative encourages community engagement by inviting users to endorse their work on GitHub, consult documentation, seek support on their community forum, and participate in advancing the FHE field through the Zama Bounty Program.