Machine learning (ML) developers frequently use interactive computational notebooks, such as Jupyter notebooks, to host code for data processing and model training. Notebooks provide a convenient tool for writing ML pipelines and interactively observing outputs. However, maintaining notebooks, e.g., to add new features or fix bugs, can be challenging due to the length and complexity of the ML pipeline code. Moreover, there is no existing benchmark related to developer edits on notebooks. In this paper, we present early results of the first study on learning for learning to edit ML pipeline code in notebooks using large language models (LLMs). We collect the first dataset of 48,398 notebook edits derived from 20,095 revisions of 792 ML-related repositories on GitHub. Our dataset captures granular details of cell-level and line-level modifications, offering a foundation for understanding real-world maintenance patterns in ML pipelines. We observe that the edits on notebooks are highly localized, with changes averaging only 166 lines of code in repositories. Although LLMs have been shown to be effective on general-purpose code generation and editing, our results reveal that the same LLMs, even after finetuning, have low accuracy on notebook editing, demonstrating the complexity of real-world ML pipeline maintenance tasks. Our findings emphasize the critical role of contextual information in improving model performance and point toward promising avenues for advancing LLMs’ capabilities in engineering ML code.