Leveraging MLOps: Developing a Sequential Classification System for RFQ Documents in Electrical Engineering
This program is tentative and subject to change.
Vendors participating in tenders face significant challenges in creating timely and accurate order quotations from Request for Order (RFQ) documents. The success of their bids is heavily dependent on the speed and precision of these quotations. A key bottleneck in this process is the time-consuming task of identifying relevant products from the product catalogue that align with the RFQ descriptions. We propose the implementation of an automatic classification system that utilizes a context-aware language model specifically designed for the electrical engineering domain. Our approach aims to streamline the identification of relevant products, thereby enhancing the efficiency and accuracy of the quotation process. However, an effective solution must be scalable and easily adjustable. Thus, we present a machine learning operations (MLOps) architecture that facilitates automated training and deployment. We pay particular attention to automated pipelines, which are essential for the operation of a maintainable ML solution. Furthermore, we outline best practices for creating production-ready pipelines and encapsulating data science efforts. Schneider Electric currently operates the solution presented in this paper.
This program is tentative and subject to change.
Thu 1 MayDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | |||
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11:45 15mTalk | aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Processing SE In Practice (SEIP) Siyuan Jiang , Jia Li Peking University, He Zong aiXcoder, Huanyu Liu Peking University, Hao Zhu Peking University, Shukai Hu aiXcoder, Erlu Li aiXcoder, Jiazheng Ding aiXcoder, Ge Li Peking University Pre-print | ||
12:00 15mTalk | Leveraging MLOps: Developing a Sequential Classification System for RFQ Documents in Electrical Engineering SE In Practice (SEIP) Claudio Martens Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Hammam Abdelwahab Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Katharina Beckh Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Birgit Kirsch Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Vishwani Gupta Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Dennis Wegener Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Steffen Hoh Schneider Electric | ||
12:15 15mTalk | On Mitigating Code LLM Hallucinations with API Documentation SE In Practice (SEIP) Nihal Jain Amazon Web Services, Robert Kwiatkowski , Baishakhi Ray Columbia University, New York;, Murali Krishna Ramanathan AWS AI Labs, Varun Kumar AWS AI Labs |