Coordinated Feature Engineering System for Real-Time ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning algorithms face challenges in generating accurate event-based predictions due to the difficulty in computing event-based feature vectors, which requires accessing event-based data that is often restricted or labor-intensive to obtain, and existing systems struggle to maintain feature values in real-time as new data arrives, affecting model accuracy.
Innovation Solution
A feature engineering system that ingests data from various sources, computes features efficiently, and maintains feature values in real-time, allowing data scientists to define feature configurations through a user-friendly interface, generating event-based feature vectors and training examples for both training and application stages, enabling accurate point-in-time predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If event-based feature vectors are computed using traditional methods, then model accuracy can be improved, but the process becomes labor-intensive and difficult to implement
Solution Approach 1:
The patent introduces an intermediary system comprising an event store, feature store, and coordinated processing system that mediates between raw event data and machine learning models. This intermediary infrastructure automates the complex task of event-based feature computation, making it accessible without requiring manual data processing expertise while maintaining high model accuracy through systematic feature engineering.
2Measurement precision
If feature values are updated in real-time as new data arrives, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the feature engineering process into distinct modular components: an event store for ingesting raw events, a feature store for maintaining computed features, and coordinated processing systems that update features as events arrive. This segmentation allows real-time feature updates to be managed through distributed, independent modules rather than a monolithic complex system, reducing overall system complexity while maintaining real-time prediction accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing feature values in the feature store before they are needed for predictions. When new events arrive, the coordinated processing system updates only the changed features rather than recomputing everything from scratch. This preliminary preparation and incremental updating approach simplifies real-time processing while maintaining accurate prediction capabilities.
3Manufacturing precision
If comprehensive event-based data is accessed to generate feature vectors, then feature quality improves, but data accessibility becomes restricted
Solution Approach 1:
The system implements self-service by automatically ingesting event data from multiple sources through the event store and autonomously computing feature vectors through coordinated processing. Data scientists can define feature configurations through a user-friendly interface without needing to manually access or process raw event data. The system serves itself by handling data ingestion, processing, and feature generation automatically, providing high-quality features while maintaining ease of operation through automated workflows.
Data Source
AI summary
A system for federated learning comprises a first computing node comprising a first database configured to store data indicative of events associated with a particular subset of a plurality of entities. The first computing node may be configured at least to receive a second set of machine learning features from a second computing node comprising machine learning features generated by data indicative of events associated with a different particular subset of a plurality of entities stored by the second computing node. The first computing node may be configured to generate a first set of machine learning features using the data indicative of events stored in the first database combined with the second set of machine learning features. The first computing node may be configured to cause a machine learning model associated with the first computing node to be trained with the first set of machine learning features.