[Video tutorial] Improve the Latency for Larger Neural Networks in Concrete ML

Concrete ML is a Privacy-Preserving Machine Learning set of tools that aims to simplify the use of Fully Homomorphic Encryption (FHE) for developers so they can automatically turn machine learning models into their homomorphic equivalent.

In this tutorial, Zama team member Jordan Frery, shows you how to improve the latency for larger neural networks in Concrete ML.

‍

Additional links

• Star the Concrete ML Github repository to endorse our work.
• Review the Concrete ML documentation.
• Get support on our community channels.
• Help us advance the FHE space with the Zama Bounty Program.