Configurable ML Pipelines for Transparent Multi-Use Deployment
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Solution Overview
Problem
Existing machine-learning processes lack transparency and flexibility, operating as 'black boxes' and requiring significant modification for different use-cases, leading to inefficiencies and impractical real-time experimentation.
Innovation Solution
A modular and configurable computational framework for training and deploying machine-learning processes using sequential execution of application engines, enabling flexible deployment across multiple use-cases and maintaining standardized process monitoring and explainability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learning processes are developed for specific use-cases, then they can achieve high performance for those specific tasks, but they become incapable of flexible deployment across multiple use-cases without significant modification
Solution Approach 1:
The patent implements a universal machine-learning process framework that can be deployed across multiple use-cases through configurable parameters and standardized interfaces. The system uses a common architecture with pluggable components that allow the same core process to handle different tasks by adjusting configuration settings rather than requiring separate customizations for each use-case.
Solution Approach 2:
The machine-learning process is divided into modular, independently configurable components that can be selectively activated or deactivated based on the specific use-case requirements. This segmentation allows the system to maintain a core universal framework while enabling flexible adaptation to different tasks through configuration rather than code modification.
2Loss of information
If machine-learning processes operate as black boxes, then they can achieve high computational efficiency, but they lack transparency regarding the importance and relative impact of input features
Solution Approach 1:
The patent introduces intermediary components that bridge the gap between the black-box machine-learning computation and the need for transparency. These intermediaries capture and process information about input feature importance and impact during the computational process, making this information available for analysis without fundamentally altering the efficient operation of the core machine-learning model.
Solution Approach 2:
The system performs preliminary analysis and capture of feature importance information during the training and inference processes, before the actual deployment decisions are made. This preliminary action ensures that transparency information is already prepared and available when needed, without requiring additional computational overhead during critical decision-making operations.
3Productivity
If significant modification is required for different use-cases, then the machine-learning processes can be highly optimized for specific tasks, but real-time experimentation becomes impractical
Solution Approach 1:
The patent implements a dynamic configuration system that allows machine-learning processes to be adjusted in real-time based on different use-case requirements. The configuration parameters can be modified on-the-fly without requiring retraining or significant code changes, enabling rapid experimentation and adaptation to new tasks while maintaining the optimized performance of the core model.
Data Source
AI summary
The disclosed embodiments include computer-implemented processes and systems that establish configurable pipelines for training and deploying machine-learning processes in distributed computing environments. For example, an apparatus may obtain elements of configuration data associated with a plurality of application engines from the memory and may execute sequentially each of a subset of the application engines in accordance with a corresponding one of the elements of configuration data. The executed subset of the application engines may perform operations that at least one of (i) train a machine-learning or artificial-intelligence process or (ii) apply the trained machine-learning or artificial-intelligence process to an input dataset. The apparatus may also transmit artifact data generated by at least one of the executed subset of the application engines to a computing system.