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- W4360910628 abstract "This paper proposes the practice of applying the culture of DevOps with machine learning to build and deploy the model rapidly and seamlessly. Without leveraging the power of DevOps and without adopting it, no industry could think of surviving today. Today, industries are dependent on old data and fetching the important data out of it, making models, and training it that help them grow their companies. That practice we call it today is ML. So, there is no possibility of surviving without being quick and ML; however, ML models take time to be developed, so to overcome that issue of time, MLOps plays a vital role today in the industries. Now, they could build it fast and grow fast. The machine learning processes initially seem easy, but if not carefully handled and designed, then creating and deploying such models may lead to huge time loss and resources. Hence, the overall performance of the system would be degraded, as well as the efficiency. This paper presents an applicable model of continuous open-source integration (CI) and continuous delivery (CD) principles and tools to minimize time wastage during system resourcing. Throughout our methodological results, we observed that the model improves the time efficiency, reduces the efforts of data scientists and cost-cutting after avoiding using heavily paid MLOps tools." @default.
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- W4360910628 date "2023-01-01" @default.
- W4360910628 modified "2023-09-23" @default.
- W4360910628 title "Automated Machine Learning Deployment Using Open-Source CI/CD Tool" @default.
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- W4360910628 doi "https://doi.org/10.1007/978-981-19-7615-5_19" @default.
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