Abstract: The present disclosure discloses a system and method for batch and real-time feature computation, and the system includes: a management server, one or more batch feature computing units, one or more distributed computing units, one or more real-time feature computing units, an offline database and a real-time database. The management server receives a script containing feature computing logics, starts batch feature computation in a batch feature computing scenario, and deploys the script in a real-time feature computing scenario to generate a real-time computing plan, wherein the script containing the feature computing logics has unity for batch feature computing logics and real-time feature computing logics, and the real-time database obtains and stores data for the real-time feature computation from the offline database. According to the present disclosure, the batch feature computing logics and the real-time feature computing logics may be unified by the script.

Abstract: A method for assisting launch of a machine learning model includes: acquiring a model file from offline training of the machine learning model; determining a training data table used in a model training process by analyzing the model file; creating in an online database an online data table having consistent table information with the training data table; and importing at least a part of offline data into the online data table.

Abstract: The present disclosure discloses a system and method for batch and real-time feature computation, and the system includes: a management server, one or more batch feature computing units, one or more distributed computing units, one or more real-time feature computing units, an offline database and a real-time database. The management server receives a script containing feature computing logics, starts batch feature computation in a batch feature computing scenario, and deploys the script in a real-time feature computing scenario to generate a real-time computing plan, wherein the script containing the feature computing logics has unity for batch feature computing logics and real-time feature computing logics, and the real-time database obtains and stores data for the real-time feature computation from the offline database. According to the present disclosure, the batch feature computing logics and the real-time feature computing logics may be unified by the script.

Abstract: Provided is a method and system for uniform execution of feature extraction. The method comprises: acquiring a feature extraction script for defining a processing logic related to feature extraction; analyzing the feature extraction script to generate an execution plan for feature extraction; and executing the generated execution plan by a local machine or a cluster based on a feature extraction scene. Based on the method and system, feature extraction can be uniformly executed at various feature extraction scenes.