Preprocessing Adapter for Heterogeneous ML Model Input Adaptation
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Solution Overview
Problem
Machine learning models often require specific input data formats, making it difficult for users to utilize existing models with disparate data sources, especially on edge devices with limited resources, which necessitates manual preprocessing and complex system development.
Innovation Solution
A preprocessing adapter determines the required input characteristics of machine learning models and performs necessary preprocessing operations to adapt data from disparate sources into appropriate formats, allowing users to utilize multiple models without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual preprocessing is performed to adapt data to model requirements, then data compatibility with machine learning models is improved, but system complexity and development time increase
Solution Approach 1:
The patent introduces a preprocessing adapter as an intermediary component that sits between data sources and machine learning models. This adapter automatically detects data characteristics, determines required transformations, and performs preprocessing operations without requiring users to manually implement complex data adaptation logic. The adapter handles format conversion, type casting, and data transformation automatically, resolving the contradiction by providing adaptability while reducing system complexity.
Solution Approach 2:
The preprocessing adapter implements self-service by automatically detecting data characteristics and determining preprocessing requirements without user intervention. It analyzes input data structures, identifies necessary transformations, and executes preprocessing operations autonomously. This self-service capability allows the system to maintain high adaptability to different data formats while eliminating the need for users to manually manage preprocessing complexity.
2Adaptability or versatility
If manual preprocessing operations are implemented, then data format adaptation to models is improved, but development time and resource consumption increase
Solution Approach 1:
The preprocessing adapter performs preliminary actions by pre-defining and pre-testing various data transformation operations. When data from disparate sources needs to be adapted to model requirements, the adapter already has prepared transformation pipelines that can be immediately applied. This eliminates the need for developers to manually create and test preprocessing code from scratch, significantly reducing development time while maintaining flexible data format adaptation.
Solution Approach 2:
The adapter performs self-service by automatically analyzing data characteristics and selecting appropriate preprocessing operations without requiring developer intervention. It detects data types, formats, and structures, then autonomously determines and executes the necessary transformations. This self-service approach maintains high data format adaptation capability while eliminating manual development time investment.
3Adaptability or versatility
If heterogeneous machine learning models are deployed, then model versatility is improved, but data preprocessing complexity increases
Solution Approach 1:
The preprocessing adapter implements universality by providing a single multi-functional component that handles preprocessing for multiple different machine learning models with heterogeneous input requirements. Rather than requiring separate preprocessing implementations for each model type, the adapter universally handles format conversion, type casting, and data transformation across diverse model architectures. This maintains model versatility while consolidating preprocessing complexity into a single manageable system.
Solution Approach 2:
The adapter serves as a universal intermediary layer between data sources and heterogeneous machine learning models. It absorbs the complexity of different model input requirements and translates diverse data formats into models-specific formats automatically. This intermediary approach allows multiple heterogeneous models to be deployed together without increasing overall preprocessing complexity, as the adapter handles all model-specific requirements uniformly.
4Reliability
If complete model regeneration is performed for environmental changes, then model accuracy is maintained, but time and computational resources increase
Solution Approach 1:
The preprocessing adapter performs preliminary actions by pre-processing and transforming data according to model requirements before the models are deployed or reloaded. When environmental changes occur and models need to be regenerated or replaced, the adapter maintains pre-established transformation pipelines that can be quickly reconfigured without requiring complete regeneration of all processing logic. This preliminary preparation reduces the time and computational resources needed for model updates while maintaining accuracy.
Data Source
AI summary
Techniques for input adaptation from disparate data sources for heterogeneous machine learning model execution are described. A preprocessing adapter can perform preprocessing of data obtained from edge devices to suit the input data characteristic requirements of one or more machine learning (ML) models. The preprocessing adapter can determine the input data characteristic requirements in a variety of ways, such as via analysis of the input layer of a ML model or through data variation testing and associated feedback resulting from output data generated by the ML model.