MLOps approach in the cloud-native data pipeline design


  • István Pölöskei Adesso Hungary Kft, Infopark sétány 1, 1117 Budapest, Hungary



MLOps, Machine learning, data pipeline, cloud-native


The data modeling process is challenging and involves hypotheses and trials. In the industry, a workflow has been constructed around data modeling. The offered modernized workflow expects to use of the cloud’s full abilities as cloud-native services. For a flourishing big data project, the organization should have analytics and information-technological know-how. MLOps approach concentrates on the modeling, eliminating the personnel and technology gap in the deployment. In this article, the paradigm will be verified with a case-study in the context of composing a data pipeline in the cloud-native ecosystem. Based on the analysis, the considered strategy is the recommended way for data pipeline design.


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How to Cite

Pölöskei, I. (2021). MLOps approach in the cloud-native data pipeline design. Acta Technica Jaurinensis, 15(1), 1–6.



Research articles