Integrated Feature Store Lineage Tracking for Model Consistency
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Solution Overview
Problem
Existing systems face challenges in maintaining relationships among data for use across multiple models, which is resource-intensive and prone to errors due to inconsistent data usage between training and deployment phases.
Innovation Solution
A feature store system that integrates tracking and management of features, including upstream and downstream lineages, to ensure consistent and efficient use of features across models, reducing the likelihood of errors and improving development efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data relationships are maintained in memory for use across multiple models, then model development efficiency is improved, but system resource consumption increases significantly
Solution Approach 1:
The patent extracts and stores data relationships in a persistent feature store database separate from model training and deployment systems. This allows multiple models to access pre-computed feature relationships without loading entire datasets into memory, reducing resource consumption while maintaining development efficiency through centralized feature management and lineage tracking.
Solution Approach 2:
The system performs preliminary computation and validation of data relationships during feature engineering phases, storing results in the feature store before model training begins. This preliminary action eliminates the need to recompute relationships for each model, improving productivity while reducing the computational burden during actual model development and deployment.
2Stability of the object's composition
If data relationships are maintained across multiple models, then consistency is improved, but error propagation risk increases
Solution Approach 1:
The patent implements automated tracking and validation mechanisms that monitor data relationships across models. When changes are made to features or data relationships, the system automatically validates consistency and alerts developers to potential errors. This feedback loop maintains data consistency while preventing error propagation through early detection and correction.
Solution Approach 2:
The feature store acts as an intermediary layer between data sources and multiple models. It maintains versioned snapshots of data relationships and provides controlled access through validation rules. This intermediary structure ensures consistent data usage across models while isolating errors to specific feature definitions rather than allowing widespread propagation.
3Adaptability or versatility
If centralized feature management is implemented, then feature reusability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal feature store that serves multiple functions: storing feature definitions, tracking lineage, validating relationships, and providing access to multiple models. This multi-functional system improves feature reusability across different models and projects while consolidating complexity into a single managed platform rather than distributing it across multiple separate systems.
Data Source
AI summary
The present application discloses a method, system, and computer system for managing a plurality of features and storing lineage information pertaining to the features. The method includes obtaining one or more datasets, determining a first feature, wherein the first feature is determined based at least in part on the one or more datasets, and storing the first feature in a feature store. The first feature is stored in association with a dataset indication of the one or more datasets from which the first feature is determined. The feature store comprises a plurality of features.