The development of machine learning programming languages is critical to support the research and deployment of ML solutions as data-size and model-complexity grow. These languages often offer built-in support for expressing machine learning models as programs and aim at automating inference, through probabilistic analysis and simulation or back-propagation and differentiation. Machine learning languages enable models to be deployed, critiqued, and improved, support reproducible research, and lower the barrier for the use of these methods.

This workshop aims to bring together researchers from both academia and industry, to discuss recent advances and challenges in machine learning languages development and research.