Announcing Concrete ML v0.2

Concrete ML has been released as a public alpha, built on top of Concrete Numpy, to enable data scientists with no cryptography knowledge to convert classical machine learning models into their Fully Homomorphic Encryption (FHE) equivalents. The release aims to simplify the adoption process for users of popular ML frameworks by providing user-friendly APIs that allow the seamless integration of FHE capabilities, as demonstrated through examples with linear models and tree-based classifiers. While tree models perform excellently with encrypted data due to Zama's Programmable Bootstrapping, linear models and neural networks currently face performance challenges, which are expected to improve with future updates and enhancements in quantization and precision. The package also supports user-provided torch models, utilizing an ONNX conversion pipeline to facilitate the use of a wide range of operators while maintaining focus on feature-completeness. As efforts continue to enhance the performance of various models under FHE constraints, Concrete ML presents a promising solution for private computations in machine learning.