Machine Learning Model Online Deployment via Data Table Consistency
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Solution Overview
Problem
Current machine learning models are ineffective in real-time online data prediction due to their offline application, leading to poor prediction performance as they fail to perceive behavior changes in a timely manner, which is critical in rapidly evolving business environments.
Innovation Solution
A method that involves acquiring a model file from offline training, determining the training data table, creating an online data table with consistent information in an online database, and importing offline data to support the online deployment of machine learning models, enabling real-time prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed offline, then model training and prediction can be performed, but the models cannot perceive behavior changes in real-time, leading to poor prediction performance
Solution Approach 1:
The patent applies preliminary action by pre-processing offline training data and creating an online data table with consistent table information before online deployment. The model file is analyzed in advance to extract training data table information, and offline data is imported into the online data table beforehand, enabling the model to quickly adapt to real-time data without extensive online processing.
Solution Approach 2:
The patent introduces an online data table as an intermediary between offline training data and real-time prediction. This online data table stores pre-processed data with consistent table information, serving as a bridge that allows the offline-trained model to efficiently access and process real-time online data without direct interaction with the original offline data sources.
2Speed
If machine learning models are brought online for real-time prediction, then timeliness of prediction is improved, but the complexity and cost of model deployment increases
Solution Approach 1:
The patent applies self-service by enabling the model deployment system to automatically analyze the model file, extract training data table information, create the online data table with consistent structure, and import offline data without requiring extensive manual configuration. This automated self-service approach significantly reduces deployment complexity while maintaining real-time prediction capability.
Solution Approach 2:
The patent reduces deployment complexity through preliminary action by pre-analyzing the model file to understand the required data table structure, pre-creating the online data table with consistent information, and pre-importing offline data before online deployment. This preparation work is done in advance, simplifying the actual online deployment process.
3Stability of the object's composition
If offline data is imported into online data table, then data consistency between offline training and online prediction is improved, but data processing time and resources increase
Solution Approach 1:
The patent applies partial action by importing only the necessary offline data into the online data table - specifically, the training data table information and relevant offline data that matches the model's training requirements. This selective partial import maintains data consistency while avoiding the time and resource costs of importing all offline data.
Data Source
AI summary
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.