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Task-Specific Automation in Deep Learning Processes

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Database and Expert Systems Applications - DEXA 2021 Workshops (DEXA 2021)

Abstract

Recent advances in deep learning facilitate the training, testing, and deployment of models through so-called pipelines. Those pipelines are typically orchestrated with general-purpose machine learning frameworks (e.g., Tensorflow Extended), where developers manually call the single steps for each task-specific application. The diversity of task- and technology-specific requirements in deep learning projects increases the orchestration effort. There are recent advances to automate the orchestration with machine learning, which are however, still immature and do not support task-specific applications. Hence, we claim that partial automation of pipeline orchestration with respect to specific tasks and technologies decreases the overall development effort. We verify this claim with the ALOHA tool flow, where task-specific glue code is automated. The gains of the ALOHA tool flow pipeline are evaluated with respect to human effort, computing performance, and security.

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Acknowledgement

This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No. 780788.

We want to thank the ALOHA Team Members for their contribution of their results and comments. Further, we want to thank Lisa Ehrlinger from SCCH for her critical eye to enforce scientific correctness and understandability.

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Correspondence to Gerald Czech .

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Buchgeher, G. et al. (2021). Task-Specific Automation in Deep Learning Processes. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2021 Workshops. DEXA 2021. Communications in Computer and Information Science, vol 1479. Springer, Cham. https://doi.org/10.1007/978-3-030-87101-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-87101-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87100-0

  • Online ISBN: 978-3-030-87101-7

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