Enhancing Machine Learning Life Cycle through Advanced Data Engineering

© 2024 by IJCTT Journal
Volume-72 Issue-4
Year of Publication : 2024
Authors : Deepak Jayabalan, Shantanu Indra
DOI :  10.14445/22312803/IJCTT-V72I4P117

How to Cite?

Deepak Jayabalan, Shantanu Indra, "Enhancing Machine Learning Life Cycle through Advanced Data Engineering," International Journal of Computer Trends and Technology, vol. 72, no. 4, pp. 136-139, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I4P117

This research paper delves into the integration of advanced data engineering techniques to optimize the Machine Learning (ML) lifecycle. In today's data-driven landscape, organizations are increasingly relying on ML models for decisionmaking and predictive analytics. However, the development and deployment of ML models involve multiple complex stages, each presenting its own set of challenges. These stages include exploratory data analysis, data preparation and feature engineering, model training and tuning, model review and governance, offline evaluation, online experimentation, and deployment. Implementing changes within the ML lifecycle can be time-consuming and resource-intensive due to dependencies, complexities, and iterative experimentation cycles. This study explores how advanced data engineering strategies can address these challenges and streamline the ML lifecycle. By synthesizing existing literature and analyzing industry case studies, the research examines the impact of data engineering interventions at each stage of the ML process.
Furthermore, the study introduces key metrics, such as time to harvest, which measure the efficiency of the ML lifecycle from data collection to model deployment. It demonstrates how employing data engineering techniques can significantly reduce the time to harvest, improving operational efficiency by up to 20%. Through a comprehensive analysis, this paper provides valuable insights into the practical implications of integrating data engineering within the ML lifecycle, highlighting opportunities for innovation and optimization in ML-driven decision-making processes.

Machine Learning, Data Engineering, Lifecycle Optimization, Data Preparation, Feature Engineering, Model Training.


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