Abstract: Inter-process serving of machine learning features from mapped memory for machine learning models is described. ML features are populated in a data structure that is serialized. State data is stored that indicates that reader process(es) are to read from a first memory mapped data file and not a second memory mapped data file. The serialized bytes are stored in the second memory mapped data file and the state data is updated to indicate that the reader process(es) are to read from the second memory mapped data file. A request is received and parsed to prepare keys from attributes of the request. Based on the state data, the serialized bytes are read from the second memory mapped data file that correspond to the keys. The serialized bytes are deserialized and copied to a data structure available to an inference algorithm.