Jakub Lengiewicz

Abstract: Data availability and advances in computing power nowadays have enabled a huge growth in Machine Learning (ML) research and practice. In this wave, many tools to build machine learning models have been developed. These tools are used by data scientists for fast model building and prototyping. But obtaining actual value from ML models requires their deployment into production environments. A repeatable, fast, and reliable deployment process is vital for successful ML workflows. To facilitate deployment, we have designed and developed a special-purpose compiler to automate the translation of ML models from their formal description into source code. The design of the compiler supports a dynamic architecture that allows many different types of models as inputs and many target programming languages as outputs. When compiling a ML model, we aim for running time efficiency of the deployed model on the target computer architecture. Therefore, not only we can deploy to several programming languages, but we also exploit specific underlying characteristics of the architecture such as multiple cores and the availability of graphic processing cards.

Oscar Castro is a postdoctoral researcher at Luxembourg Institute of Science and Technology.