International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Optimizing Machine Learning Models: A Data Engineering Perspective

Author(s) Madhukar Dharavath
Country United States
Abstract This thorough article examines how data engineering and machine learning optimization intersect,
emphasizing important tactics for improving model performance. It looks at basic topics such as cloud
infrastructure scalability, feature engineering, data quality engineering, and hyperparameter optimization.
The article illustrates how sound data engineering techniques greatly increase model correctness, lower
error rates, and quicken development timelines through the examination of numerous industry
applications and research findings. To create effective machine learning systems, the article specifically
highlights the significance of automated validation frameworks, domain-specific feature development,
methodical optimization techniques, and cloud-native architectures. It illustrates the critical importance of
data engineering in attaining superior model performance and offers practitioners practical insights for
putting into practice efficient machine learning optimization strategies by looking at real-world
applications across several industries.
Keywords Data Engineering Optimization, Machine Learning Infrastructure, Feature Engineering Automation, Hyperparameter Tuning, Cloud-Native ML Operations
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-04
Cite This Optimizing Machine Learning Models: A Data Engineering Perspective - Madhukar Dharavath - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32242
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32242
Short DOI https://doi.org/g8tv8p
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