ML pipelines, as key components of ML systems, shall follow quality assurance techniques. Unfortunately, it is often the case in which they present maintainability issues, due to the experimentation nature of data collection and ML model construction. To address this problem, this work in progress proposes initial metrics to detect code smells of ML pipelines. These metrics reflect good software engineering practices for code quality of ML pipelines.