Master of Science in Data Science


Department of Computer Science

Faculty/ School

School of Mathematics and Computer Science (SMCS)

Date of Submission

Fall 2022


Dr. Tariq Mahmood, Professor and Program Coordinator MS(CS) and MS(DS) Programs, School of Mathematics and Computer Science (SMCS)


One of the most important components of DEVOPS is called Continuous Integration & Continuous Delivery (CI/CD). CI/CD is a set of practices which developers use to deliver new features and code bug fixes more reliably by automating parts of deployment and testing. This is done in order to preserve code quality and security so the primary focus can move to addressing business objectives. In this project, we are going to simulate a CI/CD pipeline, by creating an architecture whose goal is to update a machine learning model as changes in data are detected. In this way, we demonstrate how the CI/CD pipeline facilitates rapid delivery of changes without the hassle of manual human intervention, which may result in delivery delays. A ML Ops pipeline, which also falls under CI/CD, but for machine learning applications, will allow us to deploy, maintain, and monitor the machine learning models in production. Such a pipeline, when implemented in industry, will greatly speed up the development and release of code to production. There will be a much lower rate of defects as small changes in code will be continuously pushed to production, and since most of the repetitive tasks are automated, the code delivery is more efficient.

Document Type

Restricted Access

Submission Type

Research Project

Available for download on Saturday, October 31, 2026

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