Abstract
This research paper aims to explore the integration of command-line interfaces (CLIs) in machine learning workflows to enhance accessibility, efficiency, and user experience. Traditional graphical user interfaces (GUIs) dominate the machine learning ecosystem, but CLIs offer unique advantages, particularly in resource – constrained environments and for automation purposes . The field of machine learning (ML) has witnessed rapid advancements, leading to the development of sophisticated models and algorithms. However, the accessibility and efficiency of employing these powerful tools remain significant challenges, especially for practitioners with diverse skill levels. This paper explores the role of command- line interfaces (CLIs) as a solution to address these challenges in the context of machine learning workflows. CLIs have a long-standing history in software development and system administration, providing a text-based interface for interacting with computer programs. In recent years, the integration of CLIs in the machine learning ecosystem has gained traction due to their ability to streamline and automate complex tasks This paper reviews the current landscape of ML CLIs, highlighting their advantages in terms of reproducibility , scalability and version control.
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