Model Callers for Transforming Predictive and Generative AI Applications
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 17 Apr 2024.
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6 of 17 references.
References · 6
- 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
- 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 853
- 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
- 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
- 2022Management of Machine Learning Lifecycle Artifacts: A Surveycited by 13
- 2022Quality issues in Machine Learning Software Systemscited by 7
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