Wide Table Feature Store for Machine Learning Pipelines
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Solution Overview
Problem
Existing machine learning models face inefficiencies and inaccuracies when dealing with time-sensitive data, such as housing prices, due to duplicated work in maintaining pipelines, lack of information sharing, and potential bias from unscreened training data outside the model's time period.
Innovation Solution
A wide table feature store system is implemented, allowing for efficient processing and improved sharing of information across multiple models and groups, with features served on a database for fast access, enabling faster experimentation and reducing the time to add new features from days to hours, while separating physical and logical mechanisms for managing tables and features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate pipelines are maintained for different groups/models, then each group can have customized features, but duplicated work increases and efficiency decreases
Solution Approach 1:
The patent merges separate feature pipelines into a unified feature store that serves multiple groups and models. The feature store consolidates feature definitions, transformations, and serving logic into a single shared infrastructure, eliminating duplicated work while maintaining the ability to serve different groups with their specific feature requirements through a common platform.
Solution Approach 2:
The feature store is designed as a universal system that can serve multiple groups, models, and use cases simultaneously. It provides a multi-functional platform where features can be defined once and reused across different machine learning models and groups, while still allowing customization through configuration rather than separate pipeline implementations.
2Reliability
If information is not shared across groups, then data privacy and model independence are maintained, but information sharing and feature discovery become difficult
Solution Approach 1:
The feature store acts as an intermediary layer between data sources and machine learning models. It provides a controlled interface that enables feature discovery and sharing while maintaining model independence. The system allows groups to discover and reuse features through the intermediary without direct access to each other's data or model logic, balancing information sharing with privacy and independence requirements.
3Quantity of substance
If training data includes points outside the model's time period, then more data is available for training, but bias is introduced into the model
Solution Approach 1:
The feature store implements dynamic time-aware feature serving that adapts to the model's training time period. It dynamically filters and serves only the features relevant to the specified time range, preventing bias from out-of-time data points while maintaining access to the full historical dataset when needed. This dynamic approach allows the system to adjust data availability based on the specific modeling requirements.
4Ease of manufacture
If features are added through traditional pipelines, then comprehensive feature engineering is possible, but the time to add new features increases from hours to days
Solution Approach 1:
The feature store pre-computes and stores feature transformations and aggregations in advance, making them readily available for immediate use. By performing feature engineering work beforehand and caching the results, the system eliminates the need for time-consuming pipeline execution when new features are needed, reducing feature addition time from days to hours while maintaining comprehensive feature engineering capabilities.
Data Source
AI summary
In an example embodiment, a solution is provided that produces wide tables containing features for machine learned models. These wide tables are made available for model training for multiple models and/or groups. These wide tables may be served on a serving database for fast access for application program interface (API) serving and lightweight access during interactive development. The solution decreases the time needed to add a new feature from several days to a couple of hours by enabling experimentation.