In this tutorial, Zama team member Jordan Frery, shows you how to improve the latency for larger neural networks in Concrete ML.
At Zama, our goal is not only to make FHE accessible to all developers, but also to make it extremely fast.
How to design and code a privacy-preserving version of 23andMe-like (or other DNA testing apps) using Zama Concrete ML
Concrete ML v1.6 improves latency on large neural networks and supports pre-trained tree-based models with many other improvements
In this tutorial, Zama team member Roman Bredehoft, shows you how to work with encrypted DataFrames using Concrete ML.
Concrete ML v1.5 introduces a new DataFrame API and a new option that speeds up neural networks.