Hierarchical Feature Protocol for Collaborative Machine Learning
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Solution Overview
Problem
Current analytics systems face significant challenges in feature selection and management during the creation and training of statistical models, leading to inefficiencies in memory usage, computational resources, and the risk of overfitting, as well as complexities in sharing and organizing feature sets across different teams and environments.
Innovation Solution
A hierarchical representation of features using a directed acyclic graph (DAG) within a common feature protocol, which allows for declarative definition, organization, and sharing of features across multiple execution environments, enabling collaborative machine learning by disambiguating feature names and calculating derived features through reference relationships and formulas.
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 shared across projects and teams, but storage resources are significantly occupied and synchronization complexity increases
Solution Approach 1:
The patent merges feature definitions into a single centralized feature repository that serves both offline and online execution environments. Instead of maintaining separate denormalized copies, the system consolidates feature metadata and definitions in one location, eliminating duplication and the associated synchronization overhead while maintaining adaptability across different projects and teams.
Solution Approach 2:
The centralized feature repository is designed to serve multiple execution environments (offline and online) simultaneously through a universal interface. The same feature definitions can be reused across different projects, teams, and execution contexts without requiring environment-specific copies, thereby achieving versatility without proportionally increasing storage or synchronization complexity.
2Reliability
If statistical models use large numbers of features, then more comprehensive analysis is achieved, but memory and computational resources increase significantly
Solution Approach 1:
The system extracts only the necessary features for each specific statistical model from the comprehensive feature repository. Instead of loading all available features into memory, the model selection and feature selection mechanisms identify and extract only the relevant subset of features needed for each analysis task, thereby maintaining analysis comprehensiveness while significantly reducing memory and computational resource requirements.
3Adaptability or versatility
If teams manually identify relevant features from a large list of all features, then feature selection can be customized, but overhead and time consumption increase
Solution Approach 1:
The system implements automated feature selection mechanisms that provide feedback to teams about relevant features based on historical performance, model requirements, and data characteristics. This automated feedback loop reduces the manual identification overhead by pre-filtering and ranking features, allowing teams to quickly select customized feature sets without manually evaluating every feature in the large repository.
Data Source
AI summary
The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.