Browsing by Subject "continuous integration"

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  • Mäkinen, Sasu (Helsingin yliopisto, 2021)
    Deploying machine learning models is found to be a massive issue in the field. DevOps and Continuous Integration and Continuous Delivery (CI/CD) has proven to streamline and accelerate deployments in the field of software development. Creating CI/CD pipelines in software that includes elements of Machine Learning (MLOps) has unique problems, and trail-blazers in the field solve them with the use of proprietary tooling, often offered by cloud providers. In this thesis, we describe the elements of MLOps. We study what the requirements to automate the CI/CD of Machine Learning systems in the MLOps methodology. We study if it is feasible to create a state-of-the-art MLOps pipeline with existing open-source and cloud-native tooling in a cloud provider agnostic way. We designed an extendable and cloud-native pipeline covering most of the CI/CD needs of Machine Learning system. We motivated why Machine Learning systems should be included in the DevOps methodology. We studied what unique challenges machine learning brings to CI/CD pipelines, production environments and monitoring. We analyzed the pipeline’s design, architecture, and implementation details and its applicability and value to Machine Learning projects. We evaluate our solution as a promising MLOps pipeline, that manages to solve many issues of automating a reproducible Machine Learning project and its delivery to production. We designed it as a fully open-source solution that is relatively cloud provider agnostic. Configuring the pipeline to fit the client needs uses easy-to-use declarative configuration languages (YAML, JSON) that require minimal learning overhead.
  • Kauhanen, Eero Olavi; Nurminen, Jukka K; Mikkonen, Tommi; Pashkovskiy, Matvey (IEEE, 2021)
    In this paper, we present a coverage-based regression test selection (RTS) approach and a developed tool for Python. The tool can be used either on a developer's machine or on build servers. A special characteristic of the tool is the attention to easy integration to continuous integration and deployment. To evaluate the performance of the proposed approach, mutation testing is applied to three open-source projects, and the results of the execution of full test suites are compared to the execution of a set of tests selected by the tool. The missed fault rate of the test selection varies between 0-2% at file-level granularity and 16-24% at line-level granularity. The high missed fault rate at the line-level granularity is related to the selected basic mutation approach and the result could be improved with advanced mutation techniques. Depending on the target optimization metric (time or precision) in DevOps/MLOps process the error rate could be acceptable or further improved by using file-level granularity based test selection.