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

July 24, 2024
  -  
Jordan Frery

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

Read more related posts

Concrete ML v1.6: Bigger Neural Networks and Pre-trained Tree-based Models

Concrete ML v1.6 improves latency on large neural networks and supports pre-trained tree-based models with many other improvements

Read Article

Build an End-to-End Encrypted 23andMe-like Genetic Testing Application using Concrete ML

How to design and code a privacy-preserving version of 23andMe-like (or other DNA testing apps) using Zama Concrete ML

Read Article

Build an End-to-End Encrypted Shazam Application Using Concrete ML

A tutorial on how to code a privacy-preserving version of Shazam using Zama Concrete ML.

Read Article