Machine Learning State Reuse for Fast Model Variant Generation
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Solution Overview
Problem
Implementing machine learning techniques is challenging due to algorithmic complexities, resource consumption, and the need for complete model regeneration with environmental or use case changes, differing from traditional software engineering practices.
Innovation Solution
The generation of machine learning state data structures allows for efficient creation of multiple model variants without re-examining raw training data, using a finalize and update function to generate models quickly and with reduced resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complete model regeneration is performed when environmental or use case changes occur, then model accuracy and adaptability are maintained, but time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing training data into structured representations (e.g., feature statistics, data distributions, pre-computed embeddings) that can be quickly reused. When environmental changes occur, the system updates models using these pre-computed structures rather than reprocessing raw data, significantly reducing regeneration time while maintaining adaptability.
Solution Approach 2:
The patent segments the model generation process into distinct phases: data pre-processing, model training, and deployment. By separating these stages and caching intermediate results (feature extracts, data representations), the system enables incremental updates without complete regeneration, resolving the contradiction between adaptability and time consumption.
2Reliability
If multiple model variants are generated through traditional training processes, then model performance and accuracy are optimized, but resource consumption and time requirements become extremely high
Solution Approach 1:
The patent performs preliminary processing of training data into reusable structures (feature statistics, data distributions, pre-computed embeddings) before model training. These pre-computed structures enable rapid generation of multiple model variants with different hyperparameters without reprocessing raw data, significantly improving productivity while maintaining the ability to generate accurate models.
Solution Approach 2:
The patent creates copies of pre-processed data structures and uses them across multiple model training iterations. Instead of training each model variant from scratch with raw data, the system copies and reuses the pre-computed feature representations, reducing resource consumption and time requirements while enabling parallel generation of multiple variants for optimization.
3Reliability
If machine learning models are highly specialized for particular use cases and environments, then model performance is optimized, but flexibility and ease of deployment across different environments decrease
Solution Approach 1:
The patent creates universal data representations and feature structures from training data that can serve multiple model variants and different deployment environments. By pre-processing data into environment-agnostic representations (statistical summaries, distributed embeddings), the system enables a single pre-processing pipeline to support multiple specialized models, improving both performance and environmental flexibility.
Data Source
AI summary
Techniques for model variant generation using machine learning states are described. A machine learning state data structure is generated via an analysis of training data. The machine learning state can be used to quickly and efficiently generate multiple variants of a machine learning model without needing to re-analyze the raw training data again.