ML Module Interaction Validation in CI Pipelines
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Solution Overview
Problem
Machine learning systems face challenges in correctly capturing relationships between different modules, leading to inefficiencies and decreased model performance due to incorrect module interactions.
Innovation Solution
A computing apparatus and method for managing module interactions by obtaining and testing module interaction specifications, preventing or permitting interactions based on compliance with these specifications, and performing these checks in continuous integration pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If module interaction specifications are implemented and tested, then reliability of module interactions is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by obtaining and validating module interaction specifications before the modules actually interact. The system pre-defines expected parameters, data types, and interaction contracts between modules, then automatically tests incoming interaction data against these specifications prior to processing. This prevents incompatible interactions from occurring in the first place, thereby improving reliability without requiring complex runtime validation mechanisms.
Solution Approach 2:
The patent introduces an intermediary component that acts as a mediator between interacting modules. This intermediary automatically obtains interaction data, tests it against predefined specifications, and determines whether to permit or block the interaction. By placing this intermediary layer between modules, the system improves interaction reliability through automated validation while isolating the complexity of specification management from the individual modules themselves.
2Manufacturing precision
If automatic testing of module interaction data is performed, then manufacturing precision of interactions is improved, but loss of time in the interaction process increases
Solution Approach 1:
The system performs preliminary validation by pre-defining interaction specifications including expected data types, parameters, and formats. When interaction data arrives, the system quickly checks it against these pre-established criteria rather than performing complex analysis. This preliminary action ensures high precision in detecting incompatible interactions while minimizing the time required for validation.
Solution Approach 2:
The patent applies parameter changes by transforming interaction validation from a complex analytical process into a straightforward parameter comparison task. The system converts interaction specifications into defined parameters (data types, expected formats, required fields) and automatically compares incoming interaction data against these parameters. This parameter-based approach maintains high precision in detecting incompatibilities while significantly reducing the time required for validation compared to manual or complex automated analysis.
Data Source
AI summary
Systems and methods in which a training dataset of a trained tree-based model is embedded as an array of vectors, where each dimension represents a decision point in the model; a distance between historical sample points in a time series is defined as a cosine similarity function between two of these vectors; the array of vectors is processed through a Hierarchical Navigable Small World index, thereby producing an approximate view of similar vectors; for a new prediction, there is a search for a number of most similar vectors; and a discrete probability distribution is created.