Abstract: A machine learning application method, a device, an electronic apparatus, and a storage medium, used to directly link service scenarios, aggregate data related to the service scenarios, accordingly explore modeling schemes, and ensure that data used in offline modeling scheme exploration and data used in an online model prediction service have the same source, thereby realizing consistency of source of offline and online data. Directly deploying an offline model to an online environment results in data inconsistency between online feature computation and offline feature computation, which in turn causes poor prediction performance; therefore, only a modeling scheme is deployed online, and the offline model is not deployed. After a modeling scheme is deployed online, sample data having a feature and feedback can be obtained by receiving a prediction request, thereby enabling model self-learning by means of the sample data.

Abstract: A method for performing a machine learning process implementation is provided. The method comprises includes the following. Data is obtained. A labelling result of the data is obtained. A model framework matching a requirement of a user and/or a model matching a predicted target of the user is selected. Model training is performed using the data and the labelling result of the data based on the selected model framework and/or the selected model. The model framework is used to perform the model training on the basis of a machine learning algorithm.