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About this paper
Monirul Islam Mahmud
ZeroML proposes a new compiled, multi-paradigm programming language built specifically for Automated Machine Learning (AutoML) to address performance, complexity, and deployment challenges. The language aims to simplify ML workflow development and deployment, offering native multithreading, optimized memory management, and built-in distributed computing capabilities, demonstrated to significantly reduce boilerplate code compared to Python and R-based AutoML tools.
Key insights
- Designing a domain-specific programming language for AutoML can fundamentally address limitations in performance, complexity, and deployment faced by existing library-based frameworks.
- A compiled language approach enables static type checking, machine code generation for optimal performance, and true native multithreading, overcoming bottlenecks present in interpreted languages like Python and R.
- Integrating core AutoML functionalities into the language's syntax, rather than relying on external libraries, can significantly simplify workflows and reduce boilerplate code for users.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.