| CPC G06N 5/04 (2013.01) [G06N 3/08 (2013.01); G06T 7/0004 (2013.01)] | 12 Claims |
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1. An inference computing apparatus, comprising at least one processor and a memory, wherein the memory has stored therein program instructions that, when executed by the at least one processor, cause the inference computing apparatus to perform following operations:
receiving a first inference model from a model training apparatus, the first inference model being obtained by the model training apparatus through a first model training based on a first training sample library, the first training sample library including first training samples from historical data generated in a manufacturing stage, and the model training apparatus including a cloud device;
performing an inference computing on data to be processed generated in the manufacturing stage based on the first inference model to obtain an inference result, and sending the inference result to a user-side device, the inference computing apparatus being closer to the user-side device than the model training apparatus;
evaluating a performance of the first inference model to determine whether the first inference model needs to be updated; and
updating the first inference model when the first inference model needs to be updated, wherein updating the first inference model includes:
performing a second model training based on a second training sample library to obtain a second inference model, or sending a model update request to the model training apparatus to obtain the second inference model, the second training sample library including: second training samples from the historical data, third training samples being from the inference result and subjected to re-judging, or the second training samples from the historical data and the third training samples being from the inference result and subjected to re-judging; and
updating the first inference model with the second inference model in a case where the second inference model meets an update condition, wherein the update condition includes that:
a test is performed on the second inference model and the second inference model passes the test, wherein the test includes: evaluating a performance of the second inference model based on test samples; and when the performance of the second inference model meets evaluation requirements, determining that the second inference model passes the test; and
a gray-scale deployment is performed on the second inference model, the performance of the second inference model is evaluated during the gray-scale deployment, and the performance of the second inference model meets the evaluation requirements, wherein the gray-scale deployment is that the inference computing apparatus performs a simulation processing using the second inference model within a preset period of time;
wherein before performing the second model training to obtain the second inference model, or before sending the model update request to the model training apparatus to obtain the second inference model, the inference computing apparatus further performs following operations:
determining whether training parameters required for the second model training to be performed are within a preset range of the training parameters; and when the training parameters are within the preset range, performing the second model training; when the training parameters are not within the preset range, sending the model update request to the model training apparatus, wherein the training parameters include at least one of data size, training duration and computing power required for the second model training, and the preset range of the training parameters is a range of the training parameters which corresponds to the case where a training capacity of the inference computing apparatus meets the requirements of the second model training to be performed.
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