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Towards transparent AI-Systems: Benefits of MLOps Pipelines for Space System Development

Speth, Franca and Hartmann, Carsten and Krebschull, Udo and Sabath, Dieter and Sellmaier, Florian (2022) Towards transparent AI-Systems: Benefits of MLOps Pipelines for Space System Development. In: Proceedings of the International Astronautical Congress, IAC. 73rd International Astronautical Congress (IAC 2022), 2022-09-18 - 2022-09-22, Paris, Frankreich. ISSN 0074-1795.

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Artificial Intelligence (AI) and Machine Learning (ML) have found its way into different disciplines in the space industry. At the moment the biggest AI related research area is within satellite and rover control. Currently, most of the research is still in an early development phase and has not been deployed as productive systems. Prototypes often lack transparency when it comes to the data preparation steps, data versions and model parameters, even though minor modifications in the workflow can lead to major changes in model performance. Especially in prototype phases these changes are often documented manually. These challenges resulted in a novel research area called Machine Learning Operations (MLOps). The focus of MLOps is on adjusting DevOps (known as the combination of development and operations in software engineering) principles to the complex requirements of ML. MLOps provides automation and monitoring of all steps of ML-system development and deployment. These steps include the integration, testing, release, deployment and infrastructure management of the system. A big variety of open-source tools are available offering solutions for different workflow stages in MLOps. So far continuous monitoring and continual learning or efficient logging of experiments in early development phases have not been addressed in the context of AI based systems within the space industry. In light of the recent aspirations to perform human deep space operations in the near future an increase of smart support systems can be expected. The International Space Station (ISS) and the Gateway function as important testing environments for the systems. Through the use of MLOps developers can increase reproducibility and transparency of their ML models. This work aims to demonstrate and discuss the benefits of using MLOps tools for different workflow stages. By applying a pipeline to telemetry data of the Columbus Module we extend MLOps to space applications, promoting its use in future research.

Item URL in elib:https://elib.dlr.de/192815/
Document Type:Conference or Workshop Item (Speech)
Title:Towards transparent AI-Systems: Benefits of MLOps Pipelines for Space System Development
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Hartmann, CarstenUNSPECIFIEDhttps://orcid.org/0000-0003-3701-189X147618004
Krebschull, UdoGoethe-Universität, Frankfurt am MainUNSPECIFIEDUNSPECIFIED
Date:18 September 2022
Journal or Publication Title:Proceedings of the International Astronautical Congress, IAC
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:MLOps, Machine Learning, Spacecraft Operations, Artificial Intelligence, Deep Space, System Development
Event Title:73rd International Astronautical Congress (IAC 2022)
Event Location:Paris, Frankreich
Event Type:international Conference
Event Start Date:18 September 2022
Event End Date:22 September 2022
Organizer:International Astronautical Federation (IAF)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - AI methods and technologies for on-board processing
Location: Oberpfaffenhofen
Institutes and Institutions:Space Operations and Astronaut Training > GSOC-German Space Operations Center
Deposited By: Klaas, Sabine
Deposited On:22 Dec 2022 15:18
Last Modified:24 Apr 2024 20:53

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