Systems and methods for a predictive cache of machine learning models

A method for generating a predictive cache of proximate machine learning models based on one or more trained machine learning models, comprising:

• generating an information request to a database, wherein the information request comprises a plurality of features selected by a user;
• receiving, from the database, a training dataset, wherein the training dataset comprises values for the plurality of features selected by the user;
• training one or more machine learning models using the training dataset and inserting the one or more machine learning models into a user library of machine learning models;
• without input from the user, based on a distribution of values for a first feature of the plurality of features, generating a search request to the database for labeled data having a distribution of values within a threshold of the distribution of values of the first feature;
• in response to the search request being successful, adding to the training dataset values from the labeled data to generate an updated training dataset;
• and
• training a proximate machine learning model based on the updated training dataset and inserting the proximate machine learning model into a predictive cache of machine learning models, wherein the proximate machine learning model is related to the one or more machine learning models but was trained without input from the user.

A non-transitory, computer-readable storage medium storing instructions that when executed by one or more processors cause the one or more processors to perform operations comprising:

• training one or more machine learning models using a training dataset and inserting the one or more machine learning models into a user library of machine learning models;
• without input from a user, based on a distribution of values for a first feature of a plurality of features from the training dataset, generating a search request to a database for labeled data having a distribution of values within a threshold of the distribution of values of the first feature;
• in response to the search request being successful, generating an updated training dataset by adding in the training dataset values from the labeled data or replacing values for the first feature in the training dataset with the values from the labeled data;
• and
• training a proximate machine learning model based on the updated training dataset and inserting the proximate machine learning model into a predictive cache of machine learning models, wherein the proximate machine learning model is related to the one or more machine learning models but was trained without input from the user.