Framework-Neutral Clustering Models for Cross-Language Artifact Updates
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Solution Overview
Problem
Clustering models face challenges in balancing computational efficiency during training and compatibility in different programming languages, leading to inefficiencies and high computational costs when frequently retraining is required.
Innovation Solution
Separate the training and use of clustering models into two computational environments, using different programming languages with a subset of artifacts transferred between environments for prediction, and update the artifacts instead of retraining the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a common programming language is selected for both training and prediction of clustering model, then compatibility with software within computing environments is improved, but computational efficiency deteriorates
Solution Approach 1:
The system divides the clustering model workflow into two separate computational environments: a training environment using Python with scikit-learn for efficient model training, and a prediction environment using a different programming language for deployment. This segmentation allows each environment to use the programming language best suited for its specific function, resolving the contradiction between compatibility and computational efficiency.
2Reliability
If clustering model is retrained frequently to incorporate new training data, then model effectiveness is improved, but computational cost and time required deteriorates
Solution Approach 1:
The system extracts only the essential parameters (cluster centers and cluster sizes) from the trained clustering model and transfers them to the prediction environment as artifacts. This extraction allows the model to be updated efficiently by only retraining and transferring the necessary components rather than retraining the entire model, reducing computational cost and time while maintaining model effectiveness.
3Loss of information
If all parameters of trained clustering model are transferred between computational environments, then completeness of model information is improved, but computational resources and network capacity used deteriorates
Solution Approach 1:
The system extracts and transfers only the essential parameters (cluster centers and cluster sizes) required for prediction from the trained clustering model, rather than transferring all parameters. This selective extraction reduces the amount of data transferred between environments, decreasing network capacity usage and computational resources while maintaining the completeness of information necessary for accurate prediction.
Data Source
AI summary
An example embodiment may involve receiving a representation of a parameter of a first clustering model (such as the cluster centroid in a k-means clustering model) where the representation of the parameter is associated with training data in accordance with a first set of software libraries. Possibly based on the parameter, a second clustering model in accordance with a second set of software libraries could be generated. As a consequence, the second clustering model could make a prediction result based on a received prediction request.