Abstract: Model training methods and systems are described herein. In one method for a system comprising a first server located in a private cloud used for model inference and a second server located in a public cloud used for model training, a first server obtains a first training model from the second server, and inputs input data into the first training model for model inference to obtain an inference result. The first server evaluates the first training model based on the inference result and a model evaluation metric to obtain an evaluation result of the model evaluation metric, and, in response to determining that an evaluation result of at least one model evaluation metric is less than or equal to a preset threshold, sends a retraining instruction for the first training model to the second server to instruct the second server to retrain the first training model.

Abstract: This application provides a scheduling method and apparatus, and a related device. The method includes a central cluster receiving a scheduling request sent by a first cluster, and determining a second cluster that meets the scheduling request. The central cluster indicates the first cluster to execute a task by using the second cluster. The method can support task cross-cluster scheduling, thereby implementing resource sharing between different clusters, and increasing resource utilization.