Computer-based systems and methods configured to utilize automating deployment of predictive models for machine learning tasks

A system comprising:

• a memory;
• at least one processor coupled to the memory, the processor configured to:
• build feature generation code and deploy the feature generation code to a repository;
• receive input data;
• determine, using the feature generation code, features relating to input data;
• build data grouping code and deploy the data grouping code to the repository,
• generate, using the data grouping code, training data, wherein the data grouping code is configured to determine a plurality of data groupings for the features relating to the input data;
• build modeling code and deploy the modeling code to the repository;
• generate modeling code configuration files comprising information about the format and contents of the modeling code, wherein:
• the modeling code is derived at least in part by applying one or more machine learning algorithms to the training data;
• the feature generation code, the data grouping code, the modeling code, and the modeling code configuration files are configured to be pulled from the repository by a model wrapper code;
• and
• the model wrapper code is configured to generate a model wrapper configured to, after deployment of the model wrapper:
• receive a first application programming interface (API) call comprising an input data value,
• determine a score relating to the input data value, and
• send a second API call comprising the score in response to the first API call.

A non-transitory computer readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising:

• building feature generation code and deploying the feature generation code to a repository;
• receiving input data;
• determining, using the feature generation code, features relating to input data;
• building data grouping code and deploying the data grouping code to the repository,
• generating, using the data grouping code, training data by determining a plurality of data groupings for the features relating to the input data;
• and
• building modeling code and deploying the modeling code to the repository, wherein the modeling code is derived at least in part by applying one or more machine learning algorithms to the training data, wherein:
• the feature generation code, the data grouping code, and the modeling code are configured to be pulled from the repository by a model wrapper code;
• and
• the model wrapper code is configured to generate a model wrapper configured to, after deployment of the model wrapper:
• receive a first application programming interface (API) call comprising an input data value,
• determine a score relating to the input data value, and
• send a second API call comprising the score in response to the first API call.