The integration of SQL and Python in machine learning workflows within cloud environments offers a robust solution to the challenges of big data analytics. This study explores the synergistic capabilities of SQL for efficient data extraction and Python for flexible data transformation, model development, and visualization. Utilizing cloud platforms such as AWS and Google Cloud, the research established scalable pipelines for processing large datasets, training machine learning models, and deploying them in real-time. Statistical analyses validated the reliability and accuracy of models, with Neural Networks achieving the highest performance metrics. Resource utilization tests highlighted the critical role of GPUs in accelerating computations, while scalability tests demonstrated the adaptability of the pipeline to varying data loads. The results underscore the cost-effectiveness and efficiency of integrating SQL and Python in cloud-based machine learning, offering valuable insights for businesses and researchers aiming to optimize big data workflows. This study provides a practical foundation for leveraging cloud technologies to enhance analytics capabilities and addresses future directions for tool integration and emerging technologies.
Muddarla et al. (Thu,) studied this question.
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