In diverse operating system environments, cross platform machine learning techniques are increasingly becoming the core means of enhancing data processing capabilities and model performance. This study aims to explore the integration and optimization path of cross platform machine learning technology, analyze the popular cross platform service architectures and platforms, and propose a series of integration solutions related to data interaction, model joint training, resource optimization configuration, and interface unification. At the same time, this article also explores in depth optimization measures such as improving the accuracy of machine learning algorithms, accelerating model training speed, rational resource allocation, and enhancing service robustness. By adopting these integration and optimization strategies, the performance indicators and application effectiveness of cross platform machine learning technology will be enhanced, providing theoretical basis and technical guidance for engineering applications in related fields.

Yixian Jiang. Research on Integration and Optimization Strategies of Cross-platform Machine Learning Services. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 141-148. https://doi.org/10.38007/ML.2025.050115.

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