Parallel development and deployment for machine learning models

A non-transitory computer-readable storage medium comprising instructions that, when executed by at least one processor of a computing system, cause the computing system to use a plurality of processors in parallel to develop a learning model by performing operations comprising:

• accessing a sample data set to train a learning model, wherein the learning model comprises a first learning algorithm having a number of inputs and a number of outputs, and wherein each sample of the sample data set comprises a state for each of the inputs and the outputs;
• determining a number of states for each of the inputs based on the sample data set;
• selecting a subset of the inputs;
• partitioning the sample data set into a number of partitions equal to a combined number of states of the selected inputs, wherein each of the partitions corresponds to a combined state of the selected inputs and each of the partitions includes the samples of the sample data set that exhibit a same combined state of the selected inputs corresponding to the partition;
• creating a second learning algorithm for each of the partitions, wherein the second learning algorithm for a partition comprises logic of the first learning algorithm in which the combined state of the selected inputs is equal to the combined state that corresponds to the partition, and wherein the second learning algorithm is configured to receive as input those of the inputs that are not the selected inputs;
• assigning each of the second learning algorithms to one of the plurality of processors;
• training each of the second learning algorithms on the processor assigned to the second learning algorithm using the samples of the partition corresponding to the second learning algorithm to generate trained second learning algorithms;
• receiving a plurality of operational data units comprising a state for each of a plurality of inputs corresponding to the inputs of the sample data set;
• directing each of the plurality of operational data units as input to one of the trained second learning algorithms based on a combined state of the selected inputs corresponding to the operational data unit;
• and
• generating an output for each of the plurality of the plurality of operational data units from the corresponding trained second learning algorithm.

A computing system for using a plurality of processors in parallel to develop a learning model, the computing system comprising:

• at least one processor;
• a memory comprising instructions which, when executed by the at least one processor, cause the computing system to perform operations comprising:
• accessing a sample data set to train a learning model, wherein the learning model comprises a first learning algorithm having a number of inputs and a number of outputs, and wherein each sample of the sample data set comprises a state for each of the inputs and the outputs;
• determining a number of states for each of the inputs based on the sample data set, select a subset of the inputs, and partition the sample data set into a number of partitions equal to a combined number of states of the selected inputs, wherein each of the partitions corresponds to a combined state of the selected inputs and each of the partitions includes the samples of the sample data set that exhibit a same combined state of the selected inputs corresponding to the partition;
• creating a second learning algorithm for each of the partitions, wherein the second learning algorithm for a partition comprises logic of the first learning algorithm in which the combined state of the selected inputs is equal to the combined state that corresponds to the partition, and wherein the second learning algorithm is configured to receive as input those of the inputs that are not the selected inputs;
• assigning each of the second learning algorithms to one of the plurality of processors of the computing system;
• training each of the second learning algorithms on the processor assigned to the second learning algorithm using the samples of the partition corresponding to the second learning algorithm to generate trained second learning algorithms;
• receiving a plurality of operational data units comprising a state for each of a plurality of inputs corresponding to the inputs of the sample data set;
• directing each of the plurality of operational data units as input to one of the trained second learning algorithms based on a combined state of the selected inputs corresponding to the operational data unit;
• and
• generating an output for each of the plurality of the plurality of operational data units from the corresponding trained second learning algorithm.