This project highlights the integration of MLOps principles in managing the entire machine learning lifecycle. While the focus was on building a kidney tumor detection model using transfer learning, the core objective was to emphasize the importance of project structure, automation of workflows, and the use of MLOps tools like DVC for version control and Docker for deployment. It serves as a foundation for understanding how to design scalable, maintainable, and efficient ML pipelines, ensuring reproducibility and streamlined collaboration in real-world scenarios.