Unified Feature Repository for Machine Learning
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Solution Overview
Problem
Current machine learning systems face significant challenges in feature selection, management, and sharing across environments, leading to inefficiencies in training, execution, and storage due to the complexity and overhead of handling large numbers of features, which can result in overfitting and increased computational resources.
Innovation Solution
A hierarchical representation using a directed acyclic graph (DAG) for organizing primary and derived features across different teams and environments, along with a common feature protocol for centralized and structured feature management, decoupled from specific environments, enables efficient sharing, production, and consumption of features, facilitating large-scale collaborative machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If features are denormalized and duplicated in separate feature repositories for offline and online execution environments, then features can be accessed in different environments, but storage resources are significantly occupied and synchronization across repositories becomes complex
Solution Approach 1:
The patent merges feature storage into a unified feature repository that serves both offline and online execution environments, eliminating the need for separate repositories. This consolidation reduces storage overhead and simplifies synchronization by maintaining a single source of truth for all features across different execution contexts.
Solution Approach 2:
The unified feature repository is designed to serve multiple execution environments (offline and online) simultaneously through a universal interface. The same feature storage infrastructure supports both batch processing and real-time inference workloads, eliminating the need for environment-specific feature copies while maintaining adaptability to different execution requirements.
2Ease of operation
If teams manually identify relevant features from a much larger list of features for all teams, then features can be selected for team operations, but significant time and effort are spent on feature selection
Solution Approach 1:
The system performs preliminary feature organization and tagging by metadata before teams need to select features. Features are pre-categorized, tagged with relevant metadata, and structured in the unified repository to enable efficient filtering and selection. This preliminary organization significantly reduces the time teams spend searching and identifying relevant features for their specific operations.
Solution Approach 2:
The unified feature repository acts as an intermediary layer between the comprehensive feature set and individual team needs. It provides filtering, search, and selection capabilities that mediate between the large overall feature set and specific team requirements, enabling teams to quickly identify relevant features without manually examining the entire feature catalog.
3Productivity
If the same features are identified and specified multiple times during different steps of creating, training, validating, and executing machine-learning models, then features can be used across model lifecycle, but overhead and complexity are incurred
Solution Approach 1:
The patent merges feature identification and specification into a single unified process that spans the entire machine-learning model lifecycle. Features are defined once in the unified repository and then referenced consistently across creation, training, validation, and execution phases. This eliminates the overhead of re-identifying and re-specifying the same features multiple times while maintaining traceability throughout the model lifecycle.
Solution Approach 2:
The unified feature repository provides a universal feature specification that serves all stages of the machine-learning model lifecycle. The same feature definitions and identifiers are used across model creation, training, validation, and execution, enabling consistent feature reuse without requiring separate feature specifications for each stage. This universal approach reduces management overhead while maintaining feature integrity throughout the entire workflow.
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.