Machine learning platform and pipeline for efficient data processing

A computer-implemented method for generating a standardized predictive model, the method comprising:

• selecting a set of input features from a generated and standardized feature set, the generated and standardized feature set being stored in a centralized feature store accessible to a plurality of models and comprising (i) a training feature set including normalized features from before a score date and (ii) a prediction feature set including normalized features from on or after the score date;
• designating input and output formats for the predictive model;
• generating a set of templates for training, scoring, and monitoring the predictive model using a template generator to output model templates and feature templates having executable placeholders for specifying the selected input features and the designated output formats;
• performing one or more model training experiments to generate a set of trained models, including training using the training feature set and evaluating using the prediction feature set;
• identifying a preferred predictive model from the set of trained models to be used in production;
• registering the identified preferred predictive model in a central registry that (i) is accessible to a plurality of scripts, (ii) records trained model artifacts, including model weights, and (iii) separately registers a configuration file comprising configuration parameters for the identified preferred predictive model;
• and
• automating a regular scoring and monitoring of the identified preferred predictive model by scheduling production executions on future data sets and, for each execution, performing (i) an upstream dependency check of required inputs and (ii) a score validation of outputs with centralized success/failure logging.