Machine Learning Framework for Payment Feature Compilation
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Solution Overview
Problem
Current machine learning systems for processing commercial transactions face challenges in defining features due to the use of incompatible systems, leading to inaccuracies and restrictions in model development, as features are often written in different languages for training and production, and the infrastructure for feature generation is custom and cumbersome.
Innovation Solution
A machine learning framework is developed that creates independent features based on payment processing events, compiles them into a final feature, and computes each feature separately using a computing platform, allowing for network communications to access payment information from remote storage locations, and grouping results together, enabling platform-independent feature definition and sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If features are written in different languages for training and production, then team flexibility is improved, but feature accuracy and consistency deteriorate
Solution Approach 1:
The system segments feature development into independent, modular components that can be developed separately for training and production while maintaining consistency through a unified feature definition language. Each feature is broken down into discrete units that can be independently validated and tested.
Solution Approach 2:
A universal feature definition language is implemented that serves multiple purposes: it defines features for both training and production, enables cross-validation between teams, and maintains consistency across different stages of the machine learning pipeline. This single language replaces the need for separate languages for different teams.
2Ease of operation
If custom infrastructure is used for feature generation, then feature control is improved, but system complexity and maintenance burden increase
Solution Approach 1:
An intermediary feature compilation system is introduced that sits between the feature definition language and the execution infrastructure. This intermediary handles the complexity of infrastructure management, translation, and coordination, while presenting a simplified interface to users for defining and controlling features.
Solution Approach 2:
The system implements self-service capabilities where the feature compilation and execution infrastructure automatically manages itself through declarative feature definitions. The system self-configures, self-validates, and self-executes features without requiring manual infrastructure management, reducing both complexity and maintenance burden.
3Adaptability or versatility
If separate systems are used for training and production features, then team autonomy is improved, but model accuracy deteriorates due to feature inconsistencies
Solution Approach 1:
The system segments the feature pipeline into independent but consistent components that can be developed autonomously by different teams while maintaining feature consistency through a unified definition language and automated compilation process.
Solution Approach 2:
An automated feedback mechanism is implemented where features defined in one system are automatically validated, compiled, and tested in the other system. This feedback loop ensures consistency between training and production features while allowing teams to work autonomously on their respective systems.
Data Source
AI summary
A machine learning framework and method for using the same are described. In one embodiment, the method for processing data with a machine learning framework comprises creating a plurality of features as independent features, each feature of the plurality features being based on one or more events that model a plurality of records related to payment processing information, creating a final feature that groups the plurality of features together, such that each feature of the plurality of features represents a sub-feature of the final feature, compiling the plurality of features and the final feature, computing, using a computing platform, each of the plurality of features as a separate job, including sending network related communications to access the payment processing information from one or more remote storage locations, and computing, using a computing platform, the final feature separately from computing the plurality of features, including grouping results of running each of the plurality of features together.