Machine learning (ML) has transformed various fields, highlighting the importance of early defect detection in ML programs without executing the code. While static analysis presents opportunities, existing studies have limitations. Meanwhile, notebooks have become a popular platform for developing ML prototypes. Notably, notebooks offer valuable run-time information, which can potentially enhance static analysis. In this project, we propose a semi-static analysis approach that will leverage available notebook run-time information. Our techniques will incorporate abstract interpretation with ML-based methods and support both notebooks and scripts. Our goal is to deliver efficient and effective semi-static analysis methodologies and open-source tools for the early detection of defects during coding, to enhance the productivity of ML development and the quality of ML programs.