Zero-overhead training of machine learning models with ROOT data

The ROOT software framework is widely used in High Energy and Nuclear Physics (HENP) for storage, processing, analysis and visualization of large datasets. With the large increase in usage of ML for experiment workflows, especially lately in the last steps of the analysis pipeline, the matter of exposing ROOT data ergonomically to ML models becomes ever more pressing. This contribution presents the advancements in an experimental component of ROOT that exposes datasets in batches ready for the training phase. This feature avoids the need for intermediate data conversion and can further streamline existing workflows, facilitating direct access of external ML tools to the ROOT input data in particular for the case when it does not fit in memory. The goal is to keep the footprint of using this feature minimal, in fact it represents just an extra line of code in user application. The contribution demonstrates the usage of the tool in an ML model training scenario, also evaluating the performance with key metrics.