MLOps approach in the cloud-native data pipeline design

Abstract

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.

Deception and Continuous Training Approach for Web Attack Detection using Cyber Traps and MLOps

“…To map how MLOps is currently understood and how it compares and differs from related techniques such as DevOps, the article 8 used meta-analysis, document analysis, and triangulation. This research gives up the comment that these related studies [8][9][10][11] effectively conceptualize MLOps and demonstrate that it lies at the nexus of software engineering, data engineering, DevOps, and ML. With this characteristic, MLOps is considered a potential solution for continuing to harden the robustness of WAD.…”

“…Finally, the Softmax layer transforms the result c into values between 0 and 1 as the probability y (9).…”