We introduce a novel software abstraction termed "model caller," acting as an intermediary for AI and ML model calling, advocating its transformative utility beyond existing model-serving frameworks. This abstraction offers multiple advantages: enhanced accuracy and reduced latency in model predictions, superior monitoring and observability of models, more streamlined AI system architectures, simplified AI development and management processes, and improved collaboration and accountability across AI/ML/Data Science, software, data, and operations teams. Model callers are valuable for both creators and users of models within both predictive and generative AI applications. Additionally, we have developed and released a prototype Python library for model callers, accessible for installation via pip or for download from GitHub.
@article{arxiv.2406.15377,
title = {Model Callers for Transforming Predictive and Generative AI Applications},
author = {Mukesh Dalal},
journal= {arXiv preprint arXiv:2406.15377},
year = {2024}
}