How to Deploy a Machine Learning Model With Concrete ML

Concrete ML is a set of privacy-preserving machine learning tools designed to simplify the use of Fully Homomorphic Encryption (FHE) for developers, enabling the conversion of machine learning models into their homomorphic equivalents. The release of Concrete ML v1.0.0 brought enhancements like improved performance and better model development assistance. A focus of the tool is deploying models, such as a breast cancer classification model, to AWS EC2 using FastAPI servers, facilitated by utility scripts that ease the deployment process via a command-line interface leveraging Boto3. The deployment involves creating an AWS EC2 instance, transferring files, installing dependencies, and running the server, with logs providing the URL for client access. Users can develop client applications to interface with the server, and the documentation provides guidance on using Client/Server APIs and addressing deployment issues, including potential future support for AWS ECR and ECS. Developers are encouraged to explore the documentation, contribute to the GitHub repository, and participate in the Zama Bounty Program to advance the field of FHE.