  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.
