Machine Learning (ML) has gained prominence across various fields, including data management. Rule-based components are being replaced by ML-driven counterparts that extract rules from experience. Statistical methods are giving way to approaches that learn functional dependencies, correlations, and data skewness. Learning-based techniques offer advantages, such as reducing the cost of developing and maintaining complex classical modules while tailoring behavior to individual system needs. This workshop brings together leaders from research projects and audiences from academia and industry to explore examples of utilizing ML to modernize data management. The discussed topics span the following categories: Robust and Explainable Cost Estimation and Plan Selection, Tuning with Explainable AI, Data Acquisition, and employing Radial Basis Functions for Cardinality Estimation.