STREAMLINING MACHINE LEARNING WORKFLOWS IN INDUSTRIAL APPLICATIONS WITH CLI’S AND CI/CD PIPELINES

Adrian-Ioan ARGESANU, Gheorghe-Daniel ANDREESCU

Abstract


The integration of machine learning (ML) in various organizations has become an essential aspect with a wide range of applications. However, the development and deployment of machine learning models can be time-consuming and prone to errors due to the iterative nature of the process and the constant testing and retraining of models. As ML becomes more integrated with industrial systems, the demand for controlled, reproducible and repeatable processes rises. This paper proposes a novel approach for the automation of various workflows of the ML model lifecycle via custom Command Line Interfaces (CLI) and Continuous Integration/Continuous Deployment (CI/CD) pipelines. We discuss the challenges and pitfalls of non-automated ML workflows, as well as the benefits of using the proposed toolset. We introduce our bespoke approach to CLI and CI/CD automation, highlighting timesaving as well as consistency-improvement aspects for Process & Pipeline Services’ use-case of detecting mechanically induced stress cracking in pipelines. This paper adds to the limited literature on ML lifecycle automation.

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References


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