Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era
This addresses deployment difficulties for developers in business scenarios, though it appears incremental as it builds on existing programming concepts.
The paper tackles the challenges of deploying deep learning models in real-world applications by introducing Model-based Programming, a new paradigm that treats models as basic computational units, and presents M Language to enhance efficiency in tasks like model loading and deployment.
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.