Machine learning engine service system, model training method and configuration method

Abstract

The embodiment of the invention provides a machine learning engine service system, a model training method and a configuration method. In the system, a model management module acquires corresponding target configuration information based on a target task determined by a user, and each piece of configuration information comprises network model information used by a task model; and then, based on the target configuration information, according to a target training process, a model training engine is called to train the model based on a target data set. According to the system provided by the embodiment of the invention, the configuration information of the task model comprises related information needing to be called when the task model is trained, when a user uses the task model, the user can directly call the corresponding configuration file to train the model to obtain a desired result, and code migration is not needed, so that the threshold of machine learning is reduced, and the robustness of machine learning is improved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a machine learning engine service system and a model training method and configuration method. Background technique [0002] At present, machine learning technology is widely used in various technical fields, such as: video surveillance, behavior analysis, image processing and other technical fields. [0003] In order to realize machine learning, some deep learning frameworks are provided in related technologies, such as: Caffe (Convolutional Architecture for Fast Feature Embedding, convolutional structure for fast feature embedding), TensorFlow, Pytorch, etc. [0004] Applying these deep learning frameworks for common machine learning models, such as neural network models, does not require developers to start coding from complex neural networks. You can select existing models as needed, obtain model parameters through training, or use existing models After addi...

AI Technical Summary

Problems solved by technology

In this case, it is still necessary to migrate a single sub-algorithm for each advanced machine learning algorithm, which consumes a lot of energy

[0009] It can be seen that for advanced machine learning algorithms, the deep learning framework in related technologies cannot be directly applied. R&D personnel not only need to conduct single-point technology research and development of a single sub-algorithm, but also need to perform code migration, which is less robust

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