Multi-state distributed training data creation using byte-based replication for an artificial intelligence platform

A method of generating and replicating distributed training data, the method comprising:

• generating, by a computing device comprising one or more processors, based on inputting training data into a data generation machine learning model, a plurality of different versions of the training data and a corresponding plurality of different sequence numbers;
• training, by the computing device, based on inputting the training data and the plurality of different versions of the training data into a distributed machine learning model, the distributed machine learning model to generate each of the plurality of different versions of the training data based on input of each of the corresponding plurality of different sequence numbers;
• determining, by the computing device, an estimated time to send a first version of the plurality of different versions of the training data to the one or more secondary computing devices;
• based on the estimated time meeting one or more criteria, sending, by the computing device, the first version of the training data and one or more identical copies of the distributed machine learning model to the one or more secondary computing devices;
• determining, by the computing device, a second sequence number corresponding to a second version of the plurality of different versions of the training data;
• sending, by the computing device, the second sequence number to the one or more secondary computing devices;
• and
• generating, in the one or more secondary computing devices, based on inputting the second sequence number into the identical copy of the distributed machine learning model in each of the one or more secondary computing devices, one or more copies of the second version of the plurality of different versions of the training data.

One or more non-transitory computer-readable comprising instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• generate, based on inputting training data into a data generation machine learning model, a plurality of different versions of the training data and a corresponding plurality of different sequence numbers;
• train, based on inputting the training data and the plurality of different versions of the training data into a distributed machine learning model, the distributed machine learning model to generate each of the plurality of different versions of the training data based on input of each of the corresponding plurality of different sequence numbers;
• determine an estimated time to send a first version of the plurality of different versions of the training data to the one or more secondary computing devices;
• based on the estimated time meeting one or more criteria, send the first version of the training data and one or more identical copies of the distributed machine learning model to the one or more secondary computing devices;
• determine a second sequence number corresponding to a second version of the plurality of different versions of the training data;
• send the second sequence number to the one or more secondary computing devices;
• and
• generate, in the one or more secondary computing devices, based on inputting the second sequence number into the identical copy of the distributed machine learning model in each of the one or more secondary computing devices, one or more copies of the second version of the plurality of different versions of the training data.