Announcing Concrete ML v1.0.0

Concrete ML v1.0.0 introduces significant advancements such as a stable API, enhanced inference performance, and improved user-friendly error reporting, making it easier to deploy models in cloud environments. The update aligns the Concrete ML API with Concrete, ensuring forward compatibility but possibly requiring code updates, for which a transition guide is provided. Focusing on Fully Homomorphic Encryption (FHE), the release includes better tools for designing FHE-compatible models and a new roundPBS feature that dramatically improves latency without losing accuracy by optimizing the application of activation functions. This version also introduces structured pruning in neural networks to accelerate inference times in FHE applications. Easy deployment is emphasized, with tutorials available for setting up FHE executions on AWS, and new resources such as a Hugging Face application for encrypted image filtering demonstrate the capabilities of Concrete ML. Additionally, technical articles provide insights into privacy-preserving tree-based inference and neural networks for encrypted inference, contributing to a deeper understanding of ML with FHE.