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Model-Based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era

EasyChair Preprint no. 10301

12 pagesDate: May 30, 2023


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.

Keyphrases: Deep Learning Models, fine-tuning, Model-based Programming

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Zheng Meng},
  title = {Model-Based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era},
  howpublished = {EasyChair Preprint no. 10301},

  year = {EasyChair, 2023}}
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