Systems and methods for layered training in machine-learning architectures

A computer-implemented method for layered training of machine-learning architectures, the method implemented by a training computing device including a processor coupled to a memory, the method comprising:

• receiving a plurality of data elements wherein each data element is associated with a timestamp;
• determining a training window for each model layer of a layered stack of model layers;
• determining a plurality of training data elements for each training window by identifying the data elements with timestamps corresponding to each of the training windows;
• identifying a previous checkpoint for each model layer wherein the previous checkpoint for each model layer is generated by a parent model layer;
• training each model layer with the determined training data elements for each model layer and the identified previous checkpoint for each model layer;
• generating a plurality of current checkpoints, wherein each current checkpoint of the plurality of current checkpoints is associated with a model layer;
• and
• storing the plurality of current checkpoints at the memory, wherein storing the plurality of current checkpoints further comprises:
• validating each checkpoint of the plurality of current checkpoints against the plurality of data elements;
• and
• storing validated checkpoints of the plurality of current checkpoints at the memory.

A training computing device for layered training of machine-learning architectures, the training computing device comprising a memory for storing data, and a processor in communication with the memory, said processor programmed to:

• receive a plurality of data elements wherein each data element is associated with a timestamp;
• determine a training window for each model layer of a layered stack of model layers;
• determine a plurality of training data elements for each training window by identifying the data elements with timestamps corresponding to each of the training windows;
• identify a previous checkpoint for each model layer wherein the previous checkpoint for each model layer is generated by a parent model layer;
• train each model layer with the determined training data elements for each model layer and the identified previous checkpoint for each model layer;
• generate a plurality of current checkpoints, wherein each current checkpoint of the plurality of current checkpoints is associated with a model layer;
• store the plurality of current checkpoints at the memory;
• purge the previous checkpoint for each model layer;
• and
• retrain each model layer.

A non-transitory computer-readable storage device, having processor-executable instructions embodied thereon, for layered training of machine-learning architectures, wherein the computer includes at least one processor and a memory coupled to the processor, wherein, when executed by the computer, the processor-executable instructions cause the computer to:

• receive a plurality of data elements wherein each data element is associated with a timestamp;
• determine a training window for each model layer of a layered stack of model layers;
• determine a plurality of training data elements for each training window by identifying the data elements with timestamps corresponding to each of the training windows;
• identify a previous checkpoint for each model layer wherein the previous checkpoint for each model layer is generated by a parent model layer;
• train each model layer with the determined training data elements for each model layer and the identified previous checkpoint for each model layer;
• generate a plurality of current checkpoints, wherein each current checkpoint of the plurality of current checkpoints is associated with a model layer;
• store the plurality of current checkpoints at the memory;
• and
• synchronize an external server with at least one current checkpoint associated with at least one model layer, wherein the external server serves based at least partially on the synchronized current checkpoint.