Automating Data Lineage and Pipeline Extraction
Jupyter Notebooks are widely spread in modern data science environments. They allow data professionals to create models, analyze data, and build data pipelines. With an increasing focus on research areas such as explainability and fairness in machine learning, there is a need to understand the relationship between the data and the model in ad-hoc project setups. This doctoral research aims to automate the process of extracting pipelines from Jupyter Notebooks and deriving data lineage from those pipelines without executing the notebook. The goal is to develop a set of tools that identify all datasets, transformations, models, and columns that serve model training inside a notebook without the need for humanintervention or execution of these pipelines.
