KModels: Unlocking AI for Business Applications

• URL: http://arxiv.org/abs/2409.05919v1
• Date: Sun, 8 Sep 2024 13:19:12 GMT
• Title: KModels: Unlocking AI for Business Applications
• Authors: Roy Abitbol, Eyal Cohen, Muhammad Kanaan, Bhavna Agrawal, Yingjie Li, Anuradha Bhamidipaty, Erez Bilgory,
• Abstract summary: This paper presents the architecture of KModels and the key decisions that shape it. KModels enables AI consumers to eliminate the need for a dedicated data scientist. It is highly suited for on-premise deployment but can also be used in cloud environments.
• Score: 10.833754921830154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• End-Cloud Collaboration Framework for Advanced AI Customer Service in E-commerce [10.443070269390871]
  In recent years, the e-commerce industry has seen a rapid increase in the demand for advanced AI-driven customer service solutions. Traditional cloud-based models face limitations in terms of latency, personalized services, and privacy concerns. We propose an innovative End-Cloud Collaboration framework for advanced AI customer service in e-commerce.
  arXiv  Detail & Related papers  (2024-09-20T13:46:54Z)
• xLAM: A Family of Large Action Models to Empower AI Agent Systems [111.5719694445345]
  We release xLAM, a series of large action models designed for AI agent tasks. xLAM consistently delivers exceptional performance across multiple agent ability benchmarks.
  arXiv  Detail & Related papers  (2024-09-05T03:22:22Z)
• Model Callers for Transforming Predictive and Generative AI Applications [2.7195102129095003]
  We introduce a novel software abstraction termed "model caller" Model callers act as an intermediary for AI and ML model calling. We have released a prototype Python library for model callers, accessible for installation via pip or for download from GitHub.
  arXiv  Detail & Related papers  (2024-04-17T12:21:06Z)
• Improving the Capabilities of Large Language Model Based Marketing Analytics Copilots With Semantic Search And Fine-Tuning [0.9787137564521711]
  We show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods.
  arXiv  Detail & Related papers  (2024-04-16T03:39:16Z)
• Open-Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning [23.395624804517034]
  Large Language Models (LLMs) have become instrumental in advancing software engineering (SE) tasks. The collaboration of these AI-based SE models hinges on maximising the sources of high-quality data. Data especially of high quality, often holds commercial or sensitive value, making it less accessible for open-source AI-based SE projects.
  arXiv  Detail & Related papers  (2024-04-09T10:47:02Z)
• Reusable MLOps: Reusable Deployment, Reusable Infrastructure and Hot-Swappable Machine Learning models and services [0.0]
  We introduce a new sustainable concept in the field of AI/ML operations - called Reusable MLOps. We reuse the existing deployment and infrastructure to serve new models by hot-swapping them without tearing down the infrastructure or the microservice.
  arXiv  Detail & Related papers  (2024-02-19T23:40:46Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs [71.7495056818522]
  We introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. We will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next.
  arXiv  Detail & Related papers  (2023-03-29T03:30:38Z)
• OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System [85.8338446357469]
  We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques. We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
  arXiv  Detail & Related papers  (2023-03-01T13:35:22Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and Introspection [61.571331422347875]
  We propose a data-driven approach to enhance models' signal-awareness. We combine the SE concept of code complexity with the AI technique of curriculum learning. We achieve up to 4.8x improvement in model signal awareness.
  arXiv  Detail & Related papers  (2021-11-10T17:58:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.