Machine learning clustering of training data for model training of customized machine learning models

A method to intelligently cluster training data into separate training data sets for customized machine learning (ML) model training for an ML system, the method comprising:

• accessing the training data, wherein each of the plurality of ML models are to be trained on the separate training data sets from the training data, and wherein the training data corresponds to individual data records each having a plurality of characteristics;
• determining a set of features used for the customized ML model training, wherein the set of features are associated with the plurality of characteristics;
• clustering the training data into the separate training data sets according to the set of features and the plurality of characteristics using an ML clustering technique;
• outputting the separate training data sets resulting from the clustering to the customized ML model training, wherein the separate training data sets are each associated with one or more of the set of features shared by corresponding ones of the individual data records clustered for each of the separate training data sets;
• training the plurality of ML models using the customized ML model training and the separate training data sets;
• packaging the plurality of ML models in individual data containers having computing code executable by an ML data processing platform for processing real-time data using the plurality of ML models;
• and
• configuring the ML data processing platform with the individual data containers, wherein the ML data processing platform is configured to associate the real-time data with a corresponding one of the plurality of ML models based on the one or more of the set of features shared by the corresponding ones of the individual data records in each of the separate training data sets.

A non-transitory computer-readable medium having stored thereon computer-readable instructions executable to intelligently cluster training data into separate training data sets for customized machine learning (ML) model training for an ML system, the computer-readable instructions executable to perform model training operations which comprise:

• accessing the training data, wherein each of the plurality of ML models are to be trained on the separate training data sets from the training data, and wherein the training data corresponds to individual data records each having a plurality of characteristics;
• determining a set of features used for the customized ML model training, wherein the set of features are associated with the plurality of characteristics;
• clustering the training data into the separate training data sets according to the set of features and the plurality of characteristics using an ML clustering technique;
• outputting the separate training data sets resulting from the clustering to the customized ML model training, wherein the separate training data sets are each associated with one or more of the set of features shared by corresponding ones of the individual data records clustered for each of the separate training data sets;
• training the plurality of ML models using the customized ML model training and the separate training data sets;
• packaging the plurality of ML models in individual data containers having computing code executable by an ML data processing platform for processing real-time data using the plurality of ML models;
• and
• configuring the ML data processing platform with the individual data containers, wherein the ML data processing platform is configured to associate the real-time data with a corresponding one of the plurality of ML models based on the one or more of the set of features shared by the corresponding ones of the individual data records in each of the separate training data sets.