Performance and accuracy of Python distributed neural network training frameworks: Spark vs. TensorFlow in big data modeling and prediction

The rapid proliferation of big data across various industries, such as healthcare, finance, and social media, has created an urgent need for robust and efficient distributed neural network training frameworks. These frameworks must handle vast volumes of data while ensuring high performance and accuracy in machine learning tasks. As organizations increasingly rely on machine learning models to extract actionable insights from big data, selecting the right framework becomes critical. This paper provides a comprehensive comparison of two leading distributed neural network training frameworks: Apache Spark and TensorFlow. These frameworks are widely adopted due to their scalability and flexibility in handling complex data-driven tasks. The study evaluates their performance and accuracy in big data modeling and prediction, focusing on key metrics such as training speed, model accuracy, resource utilization, and scalability. Utilizing datasets that mirror real-world applications, the study includes thorough data preprocessing, model construction, and distributed training experiments across both frameworks. The findings reveal that while TensorFlow achieves superior model accuracy, Apache Spark demonstrates better scalability and resource efficiency, particularly in large-scale data environments. These insights offer valuable guidance for researchers and industry practitioners in selecting the most appropriate framework for their specific big data applications, ensuring optimal performance and resource management.