System and method for continuous integration and deployment of service model using deep learning framework

A system for continuous integration and deployment of a service model using a deep learning framework, the system comprising:

• a plurality of edge servers configured to provide a deep learning inference service;
• a distributed deep learning training cloud comprising a plurality of distributed servers, each comprising a deep learning framework application query-based deep learning database server, and a main server configured to manage the plurality of distributed server and configured to perform distributed training for a learning model;
• a software configuration management (SCM) repository configured to automatically handle revision, version management, backup, and rollback processes of a service model table, which is an outcome of a service model that is the learning model subjected to distributed training;
• and
• a controller configured to, in accordance with a predetermined deployment policy, deploy the service model table to be executed on the edge servers when changes to the service model table occur in the SCM repository,
• wherein the controller is configured to transmit, in accordance with a predetermined model update policy, a train query and a training dataset of a service model of a specific function for a deep learning inference service for a specific function performed on a first edge server among the plurality of edge servers to the distributed deep learning training cloud and
• the distributed deep learning training cloud is configured to select a service model table for the service model, perform distributed training using the training dataset to generate an updated service model table, and store the updated service model table in the SCM repository.