Feature Store Lineage Tracking for Consistent Model Access
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Solution Overview
Problem
Maintaining relationships among data in memory for use across multiple models is resource-intensive and creates challenges in efficiently managing and accessing features in big data processing systems.
Innovation Solution
A feature store system that integrates a database layer for storing features, a feature catalog for managing feature metadata, and a business application layer for user interaction, enabling efficient storage, management, and retrieval of features, along with integrated tracking of feature use and lineage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data relationships are maintained in memory for use across multiple models, then access speed is improved, but resource consumption increases
Solution Approach 1:
The patent implements a feature store that stores features locally in a dedicated database structure, allowing each model to access features from a centralized repository rather than maintaining copies in memory. This local storage approach provides fast access through optimized data structures while consuming minimal memory resources, as features are retrieved on-demand from the feature store rather than being pre-loaded into memory.
Solution Approach 2:
The feature store acts as an intermediary layer between raw data sources and multiple models. It pre-processes and stores features in an optimized format, serving as a mediator that eliminates the need for models to directly access and process raw data. This intermediary structure enables fast feature retrieval while reducing the memory burden on individual models, as the feature store handles the computational overhead of feature engineering.
2Stability of the object's composition
If features are stored and managed across multiple models, then data consistency is improved, but system complexity increases
Solution Approach 1:
The patent segments the feature management system into distinct functional layers: a feature store for storage, a feature registry for metadata management, and model services for consumption. This segmentation allows each component to handle specific aspects of feature management independently, reducing overall system complexity while maintaining data consistency through standardized interfaces between layers.
Solution Approach 2:
The feature store implements a universal interface that serves multiple models simultaneously, allowing a single feature repository to support diverse modeling workflows. This multi-functional design enables the same feature storage mechanism to serve classification, regression, clustering, and other model types, reducing complexity by eliminating the need for separate feature management systems for each model.
3Loss of information
If feature lineage tracking is implemented, then data traceability is improved, but processing overhead increases
Solution Approach 1:
The patent implements preliminary action by capturing feature lineage metadata at the moment of feature creation and storage in the feature store. Instead of tracking lineage through subsequent processing stages, the system records the source data, transformation logic, and creation timestamp upfront. This preliminary capture of lineage information eliminates the need for continuous tracking during model training and inference, reducing processing overhead while maintaining complete traceability.
Solution Approach 2:
The system creates lightweight copies of lineage information in the form of metadata descriptors that reference the original feature definitions. Rather than duplicating entire feature datasets for tracking purposes, the patent stores compact metadata records that capture essential lineage information (source, transformation, version) without the computational burden of full feature replication. These metadata copies enable rapid lineage queries without impacting model training performance.
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.