Method and system for building a machine learning model

A build system for automatically building a plurality of machine learning models, comprising:

• a network controller;
• at least one processor;
• and
• a storage medium storing instructions that, when executed, configure the at least one processor to perform operations comprising:
• obtaining a design specification for the plurality of machine learning models, the design specification designating at least one of a machine learning model type to be built, one or more sources of build data, one or more sources of scoring data and score data, and a deployment cycle;
• automatically, and without human intervention, retrieving build data from the one or more sources of build data, the build data comprising training data and validation data;
• automatically, and without human intervention, constructing each of the plurality of machine learning models based on the respective machine learning model type, the training data, and the validation data to obtain a plurality of constructed machine learning models;
• automatically, and without human intervention, designating, from the plurality of constructed machine learning models, a champion and at least one challenger;
• automatically, and without human intervention, deploying the plurality of constructed machine learning models including the champion and the at least one challenger;
• automatically, and without human intervention, retrieving scoring data from the one or more sources of scoring data;
• automatically, and without human intervention, generating prediction data over the deployment cycle based on the scoring data;
• automatically, and without human intervention, retrieving score data over the deployment cycle from the one or more sources of score data;
• automatically, and without human intervention, computing one or more performance metrics based on the prediction data generated by each of the plurality of machine learning models and the score data to evaluate performances of the plurality of machine learning models;
• in response to the deployment cycle ending, automatically, and without human intervention, updating the build data based on the scoring data and the score data to obtain updated build data, wherein the updated build data comprises updated training data and updated validation data;
• automatically, and without human intervention, rebuilding each of the plurality of constructed machine learning models based on the updated training data and the updated validation data to obtain a plurality of rebuilt machine learning models;
• and
• automatically, and without human intervention, designating a new champion and at least one new challenger from the plurality of rebuilt machine learning models based on the one or more performance metrics.