Improve the Latency for Larger Neural Networks in Concrete ML

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

Concrete ML is a Privacy-Preserving Machine Learning (PPML) open-source set of tools built on top of Concrete by Zama. It aims to simplify the use of fully homomorphic encryption (FHE) for data scientists to help them automatically turn machine learning models into their homomorphic equivalent. Concrete ML was designed with ease-of-use in mind, so that data scientists can use it without knowledge of cryptography. Notably, the Concrete ML model classes are similar to those in scikit-learn and it is also possible to convert PyTorch models to FHE.

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