KModels: Unlocking AI for Business Applications
<|reference_start|>KModels: Unlocking AI for Business Applications: As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications.   KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control.   This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments.   The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.<|reference_end|>
@article{abitbol2024kmodels:,
    title={KModels: Unlocking AI for Business Applications},
    author={Roy Abitbol (1), Eyal Cohen (1), Muhammad Kanaan (1), Bhavna Agrawal
  (2), Yingjie Li (2), Anuradha Bhamidipaty (2), Erez Bilgory (1) ((1) IBM
  Research Israel, (2) IBM Research USA)},
    journal={arXiv preprint arXiv:2409.05919},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05919},
    primaryClass={cs.SE cs.AI}
}