Secondly, the data management backbone of RamanSPy ensures a direct data flow to the rest of the Python ecosystem, i.e., data can be loaded, preprocessed, and analysed in RamanSPy and then exported to conduct further modelling and analysis elsewhere (Fig. ). As an example application, we perform AI-based bacteria identification using Raman measurements from 30 bacterial and yeast isolates (Fig. ). After loading and exploring the spectra with RamanSPy, we interface the data with the lazypredict Python package and benchmark 28 different ML classification models (including logistic regression, support vector machines and decision trees) on the task of predicting the species from the spectrum. The models were trained on a high-SNR dataset (100 spectra per isolate) and tested on an unseen high-SNR testing set of the same size. Our benchmarking analysis in Fig. finds logistic regression as the best-performing model, achieving a classification accuracy of 79.63% on the species-level classification task (Fig. ), and 94.63% for antibiotic treatment classification (Fig. ).