In the realm of machine learning (ML), constructing end-to-end experimentation pipelines with scalability, robustness, and reproducibility is essential for advancing ML applications. This project is dedicated to empowering Data Scientists and ML Engineers by providing a seamless pipeline execution experience, eliminating obstacles such as downtime, hardware unavailability, OS conflicts, or dependency issues. The project not only addresses the challenges of designing scalable and reproducible ML workflows but also provides practical insights through industrial case studies, showcasing the tangible benefits of adopting such workflows.

https://github.com/ioanna123/music-genre-mlops

https://polynoe.lib.uniwa.gr/xmlui/handle/11400/6154