  This paper introduces and explores a new programming paradigm, Model-based
Programming, designed to address the challenges inherent in applying deep
learning models to real-world applications. Despite recent significant
successes of deep learning models across a range of tasks, their deployment in
real business scenarios remains fraught with difficulties, such as complex
model training, large computational resource requirements, and integration
issues with existing programming languages. To ameliorate these challenges, we
propose the concept of 'Model-based Programming' and present a novel
programming language - M Language, tailored to a prospective model-centered
programming paradigm. M Language treats models as basic computational units,
enabling developers to concentrate more on crucial tasks such as model loading,
fine-tuning, evaluation, and deployment, thereby enhancing the efficiency of
creating deep learning applications. We posit that this innovative programming
paradigm will stimulate the extensive application and advancement of deep
learning technology and provide a robust foundation for a model-driven future.
