Systems, methods, and apparatuses for implementing machine learning model training and deployment with a rollback mechanism

Abstract

In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing machine learning model training and deployment with a rollback mechanism within a computing environment. For example, an exemplary machine learning platform includes means for receiving training data as input at the machine learning platform, in which the training data includes a multiple transactions, each of the transactions specifying a plurality of features upon which to make a prediction and a label representing a correct answer for the plurality of features according to each respective transaction; specifying a model to be trained by the machine learning platform using the training data, in which the model includes a plurality of algorithms and source code; generating a new predictive engine variant by training the model to algorithmically arrive upon the label representing the correct answer as provided with the training data based on the plurality of features for each of the multiple transactions; versioning the new predictive engine variant based at least on the time the new predictive engine variant was generated a version of the source code utilized within the model and the training data received as input; deploying the new predictive engine variant into a production environment to replace a prior version of the predictive engine variant; and rolling back the new predictive engine variant from the production environment to a specified version which is less than a version of the new predictive engine variant. Other related embodiments are disclosed.