| Autor: |
Ayala Martínez, Claudia Patricia, Bilalli, Besim, Gómez Seoane, Cristina, Martínez Fernández, Silverio Juan |
| Přispěvatelé: |
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Facultat d'Informàtica de Barcelona, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
Machine Learning based Software Systems (MLSS) are becoming increasingly pervasive in today’s society and can be found in virtually every domain. Building MLSS is challenging due to their interdisciplinary nature. MLSS engineering encompasses multiple disciplines, of which Data Engineering and Software Engineering appear as most relevant. The DOGO4ML project aims at reconciling these two disciplines for providing a holistic end-to-end framework to develop, operate and govern MLSS and their data. It proposes to combine and intertwine two software cycles: the DataOps and the DevOps lifecycles. The DataOps lifecycle manages the complexity of dealing with the big data needed by ML models, while the DevOps lifecycle is in charge of building the system that embeds these models. In this paper, we present the main vision and goals of the project as well as its expected contributions and outcomes. Although the project is in its initial stage, the progress of the research undertaken so far is detailed. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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