[Video tutorial] How To Convert a scikit-learn Model Into Its Homomorphic Equivalent

Concrete ML is a set of privacy-preserving machine learning tools designed to simplify the use of Fully Homomorphic Encryption (FHE) for developers, enabling them to automatically convert machine learning models into their homomorphic equivalents. In a video tutorial, Roman Bredehoft, a machine learning engineer at Zama, demonstrates the straightforward process of transforming a scikit-learn model into its homomorphic form using Concrete ML. The initiative encourages developers to engage further by starring the Concrete ML GitHub repository, reviewing its documentation, participating in community support channels, and exploring the Zama Bounty Program, which offers opportunities to learn FHE, contribute to advancements in the field, and earn rewards.