| 主权项 |
1. A system, comprising:
one or more computing devices configured to:
generate consistency metadata to be used for one or more training-and-evaluation iterations of a machine learning model, wherein the consistency metadata comprises at least a particular initialization parameter value for a pseudo-random number source;sub-divide an address space of a particular data set of the machine learning model into a plurality of chunks, including a first chunk comprising a first plurality of observation records, and a second chunk comprising a second plurality of observation records;retrieve, from one or more persistent storage devices, observation records of the first chunk into a memory of a first server, and observation records of the second chunk into a memory of a second server,select, using a first set of pseudo-random numbers, a first training set from the plurality of chunks, wherein the first training set includes at least a portion of the first chunk, wherein observation records of the first training set are used to train the machine learning model during a first training-and-evaluation iteration of the one or more training-and-evaluation iterations, and wherein the first set of pseudo-random numbers is obtained using the consistency metadata; andselect, using a second set of pseudo-random numbers, a first test set from the plurality of chunks, wherein the first test set includes at least a portion of the second chunk, wherein observation records of the first test set are used to evaluate the machine learning model during the first training-and-evaluation iteration, and wherein the second set of pseudo-random numbers is obtained using the consistency metadata. |
