Abstract
Deep learning has been widely applied for computer vision, natural language processing, and information retrieval etc. Using a deep learning framework can reduce learning curve of beginners facilitating them to get involved with deep learning algorithms. Current deep learning frameworks can mainly be divided into traditional local deployment and cloud-based platforms. However, the two forms cannot be considered at the same time in terms of debugging and remote access. This paper focuses on the logical isolation between deep learning algorithm design and actual business execution, and it proposes an elastic framework that can resolve the contradiction between internal improvement and external access, which can improve the efficiency of both algorithm design researchers and business requirements department engineers.
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Acknowledgement
This work has received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 701697, Major Program of the National Social Science Fund of China (Grant No. 17ZDA092), Basic Research Programs (Natural Science Foundation) of Jiangsu Province (BK20180794), 333 High-Level Talent Cultivation Project of Jiangsu Province (BRA2018332) and the PAPD fund.
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Sun, M., Yang, Z., Wu, H., Liu, Q., Liu, X. (2019). An Approach to Deep Learning Service Provision with Elastic Remote Interfaces. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11634. Springer, Cham. https://doi.org/10.1007/978-3-030-24271-8_25
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DOI: https://doi.org/10.1007/978-3-030-24271-8_25
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