Oracle Machine Learning for Python (OML4Py) represents a significant advancement in data science and machine learning by seamlessly integrating Python’s extensive machine learning libraries with Oracle Database’s robust infrastructure. This paper explores the architecture, key features, and practical applications of OML4Py, highlighting its ability to execute Python scripts and machine learning algorithms directly within the database environment. By reducing data movement and leveraging the database’s processing power, OML4Py enhances efficiency and scalability in data analysis workflows. We provide a detailed guide on implementing OML4Py, from installation and configuration to model training and deployment. A case study on predicting customer churn in the telecom industry demonstrates the practical benefits and performance improvements achieved with OML4Py. This integration not only streamlines the machine learning process but also ensures secure, high-performance data processing, making it a valuable tool for data scientists and developers working with large-scale data.