Generating a decision tree model during query execution via a relational database system

A database system comprising:

• a plurality of computing device clusters, wherein a computing device cluster of the plurality of computing device clusters includes a plurality of computing devices, wherein a computing device of the plurality of computing devices includes a plurality of computing nodes, and wherein a computing node of the plurality of computing nodes includes a plurality of processing core resources;
• wherein a set of computing nodes of the pluralities of computing nodes is operable to:
• obtain a plurality of queries that include a plurality of training query operations regarding training of a plurality of machine learning models, wherein a first a query of the plurality of queries includes a first training query operation of the plurality of training query operations regarding a first machine learning model of the plurality of machine learning models;
• identify a plurality of training data based on the plurality of training query operations;
• wherein pluralities of sets of processing core resources of the set of computing nodes is operable to:
• receive the plurality of training query operations from the set of computing nodes;
• in a distributed manner, receive the plurality of training data from the set of computing nodes;
• execute, substantially in parallel, a first training query operation of the plurality of training query operations on at least a portion of a first machine learning model of the plurality of machine learning models based on respective sub-sets of sets of first training data of the plurality of training data to produce a plurality of first partial training results;
• execute, substantially in parallel, a second training query operation of the plurality of training query operations on at least a portion of a second machine learning model of the plurality of machine learning models based on respective sub-sets of sets of second training data of the plurality of training data to produce a plurality of second partial training results;
• and
• wherein the set of computing nodes is further operable to:
• compile the plurality of first partial training results to produce a first training result;
• when the first training result compares favorably to a first training threshold, update the first machine learning model based on the first training result;
• compile the plurality of second partial training results to produce a second training result;
• and
• when the second training result compares favorably to a second training threshold, update the second machine learning model based on the second training result.