
MLOps (Machine Learning Operations) systems have emerged as a crucial infrastructure in managing the lifecycle of ML projects, enabling practitioners to seamlessly transition their work from development to production in a robust and reproducible manner. However, a pertinent question arises: Is ML a unique practice that requires its own approach, distinct from traditional DevOps methodologies? This article aims to explore










