With the rapid progress of intelligent technology, machine learning is increasingly widely used in all walks of life, and the role of data engineering is increasingly prominent, becoming the core link that determines the efficiency and practicability of the model. The quality, purification, storage and control of data are directly related to the quality and speed of model training. The optimization of techniques, such as feature extraction, hyperparameter fine-tuning, regularization processing, etc., continues to promote the leap in model performance. Data engineering is not only the basis of model training, but also plays an indispensable role in the subsequent steps of model deployment, monitoring and iterative upgrading. This paper introduces the practical application effect of machine learning model enhanced by means of feature engineering, distributed computing, big data environment, etc., in order to promote the popularization and deepening development of intelligent technology.

Xindi Wei. Optimization of Machine Learning Models and Application Supported by Data Engineering. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 117-124. https://doi.org/10.38007/ML.2025.050112.

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