Integrating Engineering Practices Into Data is a B.Tech project topic for Information Technology. It gives students a clear starting point for research, implementation planning, and documentation.
Integrating Engineering Practices Into Data Project Details
| Abstract |
Data Science (DS) has emerged as a critical enabler for enhancing organizational performance through advanced analytical methodologies. Despite the existence of various Data Science Process Models (DSPMs) designed to guide DS initiatives, their practical adoption remains limited, contributing to a high rate of project failures. A significant gap exists in contemporary DSPMs, as they often do not adequately address the rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML). In response to this challenge, ML Operations (MLOps) has evolved as a framework to provide structured guidance for the development, deployment, and operationalization of ML systems. This project proposes the development and implementation of an MLOps-based DSPM (MLOps-DSPM). This model
is constructed through a systematic synthesis of 34 identified design requirements and is formalized using established modeling notations. The proposed MLOps-DSPM aims to integrate robust engineering practices into the data science lifecycle, thereby enhancing project success rates and facilitating the effective operationalization of ML systems within an organizational context. The project would involve designing the process model and potentially demonstrating its application through a practical case study.
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| Reference Paper |
Integrating Engineering Practices Into Data Science: An Mlops-Based Process Model |
| Domain |
Information Technology |
| Sub-Domain |
Data Analytics |
| PDF Download |
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