DISTRIBUTED MACHINE LEARNING ARCHITECTURE WITH HYBRID DATA NORMALIZATION, PROOF OF LINEAGE AND DATA INTEGRITY

A system, apparatus and method for processing observational data for training a neural network model for use by a neural network. Observational data is parsed into raw data and metadata components and then stored separately. To train the model, a DETL query is used to identify any raw data that may be relevant to training the model. The DETL query is processed by a metadata storage system to match any relevant metadata which, in turn, identifies raw data stored in a raw data storage system. The identified raw data is used to train the neural network model, and a updated neural network model is produced. Each time the neural network model is trained, the relevant raw data and metadata used for each training run is stored in association with the model version so that a lineage of the training may be memorialized.