Abstract: Embodiments generate machine learning recommendations using log data. Log data can be ingested to generate an event stream for cloud systems, where each of the cloud systems comprises a combination of components, and the cloud systems present heterogenous system architectures. The generated event streams can be processed to generate a data set, where the data set include issue labels for issues experienced by the cloud systems. Features from the generated data set can be extracted. Issue recommendations can be generated using machine learning algorithms based on the extracted features and the generated data set, where the issue recommendations are generated using a hybrid of collaborative based machine learning filtering and content based machine learning filtering.