Abstract
Currently, artificial intelligence (AI) and machine learning (ML) are central in many discussions. Unfortunately, their use is predominantly limited to specialists in the field. To broaden the application of AI, the barriers to its usage need to be substantially reduced. The challenge for companies planning to use AI lies in the need to hire qualified experts and invest in expensive, powerful hardware. These issues are addressed in the Open Space for Machine Learning (OS4ML) platform, that is developed in this project. The open-source solution emphasizes user-friendliness, enabling domain experts without AI technical skills to apply machine learning to their data. This method eliminates the necessity for costly and time-consuming individual AI projects.
The platform is based on Kubernetes and uses a microservices architecture, ensuring flexibility and scalability. It incorporates various powerful open-source tools, setting a standard for scalable AI applications. A key step in democratizing AI is the transition from low-code ML tools to no-code tools. This involves the development of a user-friendly frontend, which makes AI more accessible to a wider audience by removing the need for extensive coding knowledge.
The platform is designed to be adaptable to any cloud environment and is easy to set up by the community. This strategy leverages cloud computing benefits, such as scalability and cost efficiency, and provides the option for users to host the platform on their premises, a crucial feature for handling sensitive data.
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References
Dosovitskiy, A. et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (2021)
He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowl.-Based Syst. 212, 106622 (2021)
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(185), 1–52 (2018)
Li, L., et al.: A system for massively parallel hyperparameter tuning (2020)
Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J.E., Stoica, I.: Tune: a research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118 (2018)
Mayer, M., Schuster, A., Brandt, L., Deden, D., Fischer, F.: Integral quality assurance method for a CFRP aircraft fuselage skin: gap and overlap measurement for thermoplastic AFP. In: Silva, F.J.G., Pereira, A.B., Campilho, R.D.S.G. (eds.) Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems, pp. 525–534. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-38241-3_59
Molino, P., Dudin, Y., Miryala, S.S.: Ludwig: a type-based declarative deep learning toolbox (2019)
Molino, P., Ré, C.: Declarative machine learning systems (2021)
Rall, D., Bauer, B., Fraunholz, T.: Towards democratizing AI: a comparative analysis of ai as a service platforms and the open space for machine learning approach. In: Proceedings of the 2023 7th International Conference on Cloud and Big Data Computing, ICCBDC ’23, pp. 34–39, New York, NY, USA (2023). Association for Computing Machinery
Rall, D., Fraunholz, T., Bauer, B.: AI-democratization: from data-first to human-first AI. In: Central European Conference on Information and Intelligent Systems, pp. 261–67. Faculty of Organization and Informatics Varazdin (2023)
Ridnik, T., Ben-Baruch, E., Noy, A., Zelnik-Manor, L.: Imagenet-21k pretraining for the masses (2021)
Rossi, F., Cardellini, V., Presti, F.L.: Hierarchical scaling of microservices in kubernetes. In: 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pp. 28–37 (2020)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Vaswani, A., et al.:. Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wu, B. et al.: Visual transformers: token-based image representation and processing for computer vision (2020)
Acknowledgments
OS4ML is a project of the WOGRA AG research group in cooperation with the German Aerospace Center and is funded by the Ministry of Economic Affairs, Regional Development and Energy as part of the High Tech Agenda of the Free State of Bavaria.
The developed framework is open source and can be downloaded under: https://github.com/WOGRA-AG/Os4ML
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Rall, D. et al. (2024). OS4ML: Open Space for Machine Learning. In: Wang, YC., Chan, S.H., Wang, ZH. (eds) Flexible Automation and Intelligent Manufacturing: Manufacturing Innovation and Preparedness for the Changing World Order. FAIM 2024. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-74482-2_6
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DOI: https://doi.org/10.1007/978-3-031-74482-2_6
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