Sequential Application Engines for Transparent ML Deployment
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Solution Overview
Problem
Existing machine-learning processes lack transparency regarding the importance and relative impact of input features, are inflexible, and require significant modification for deployment across multiple use-cases, often relying on hand-coded scripts that hinder real-time experimentation and practical use by developers of varying skill levels.
Innovation Solution
A modular and configurable computational framework that facilitates end-to-end training, validation, and deployment of machine-learning processes through a sequential execution of application engines, using configurable pipeline-specific scripts, ensuring compliance with process and regulatory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learning processes are developed for specific use-cases with hand-coded scripts, then they can achieve functional requirements, but they lack flexibility and require significant modification for deployment across multiple use-cases
Solution Approach 1:
The system segments the machine-learning process into distinct application engines (data retrieval, preprocessing, feature generation, model training, evaluation) that can be independently configured and executed in sequences. This modular architecture allows different combinations of engines to be assembled for different use-cases without modifying the core code, thereby improving deployment flexibility while reducing modification complexity.
Solution Approach 2:
The patent creates a universal framework where a single set of application engines can serve multiple use-cases through configurable parameters and metadata. The same data retrieval engine, for example, can fetch data for different models and use-cases by receiving different configuration inputs, eliminating the need for separate hand-coded scripts for each use-case and enabling multi-functionality.
2Loss of information
If machine-learning processes operate as black boxes, then they can achieve computational efficiency, but they lack transparency regarding input feature importance and impact
Solution Approach 1:
The patent introduces intermediate components between the black-box machine-learning models and the users, specifically application engines that generate and manage metadata, feature importance scores, and explainability information. These intermediaries capture and structure information about input feature impacts without significantly slowing down the core computational processes, thus maintaining efficiency while improving transparency.
3Ease of operation
If hand-coded scripts are used for machine-learning processes, then developers can implement specific logic, but real-time experimentation is hindered and accessibility to developers of varying skill levels is reduced
Solution Approach 1:
The system enables self-service experimentation by providing developers with configurable interfaces and metadata-driven workflows that allow them to experiment with different machine-learning configurations without needing deep programming expertise. Developers can modify parameters, swap application engines, and run experiments through configuration files rather than rewriting code, making the system accessible to developers of varying skill levels and enabling rapid real-time experimentation.
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 configuration data associated with a plurality of application engines, and pipelining data characterizing a sequential execution of at least a subset of the application engines. Based on the pipelining data, the apparatus may execute sequentially a subset of the application engines in accordance with the configuration data, which may cause the apparatus to perform operations that at least one of (i) train a machine-learning process or (ii) apply the trained machine-learning process to an input dataset. The apparatus may obtain artifact data generated by the executed subset of the application engines, store the artifact data within a data repository, and transmit a portion of the artifact data to a computing system that generated at least one element of the configuration data.