Automated Feature Engineering Orchestrator
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Solution Overview
Problem
The process of identifying features for machine learning models is cumbersome and time-consuming, requiring significant manual effort, especially in applications like face recognition, transaction verification, and autonomous vehicles, where complex data analytics are necessary for pattern recognition and classification.
Innovation Solution
An end-to-end feature selection system is introduced, comprising a feature orchestrator for managing and routing data and features, and a feature store for identifying, ranking, and storing optimal features, along with a training system for scoring and recommending features, enabling automated feature selection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature identification is used for machine learning models, then feature selection can be performed with existing tools, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system enables automated feature engineering where the machine learning model itself identifies and selects features from the data without requiring manual intervention. The model performs self-service by automatically determining which features are most relevant for the given task, eliminating the time-consuming manual feature identification process while maintaining or improving feature selection accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of feature identification with an automated computational system. Instead of manually examining and selecting features, the system uses the trained machine learning model to automatically identify relevant features through computational analysis of the data and model performance metrics.
2Ease of manufacture
If manual feature identification is used, then existing tools can be utilized, but the process requires significant manual effort and updating
Solution Approach 1:
The system enables automated feature engineering where the machine learning model itself identifies and selects features from the data without requiring manual intervention. The model performs self-service by automatically determining which features are most relevant for the given task, eliminating the time-consuming manual feature identification process while maintaining or improving feature selection accuracy.
3Productivity
If automated feature selection is implemented, then manual work is reduced, but system complexity increases
Solution Approach 1:
The system employs a universal framework that can handle different machine learning models, data types, and feature engineering tasks through a single automated process. The feature selection mechanism is model-agnostic and can be applied across various ML algorithms and domains, reducing the need for multiple specialized tools while maintaining high productivity.
Solution Approach 2:
The system introduces an intermediary feature selection layer between data ingestion and model training that automatically identifies relevant features. This intermediary component simplifies the overall process by handling feature engineering tasks centrally, reducing the complexity burden on individual components while improving overall productivity.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, devices, and the like for an end-to-end solution to auto-identifying features. In one embodiment, a novel architecture is presented that enables the identification of optimal features and feature processes for use by a machine learning model. The novel architecture introduces a feature orchestrator for managing, routing, and retrieving the data and features associated with analytical job request. The novel architecture also introduces a feature store designed to identify, rank, and store the features and data used in the analysis. To aid in identifying the optimal features and feature processes, a training system may also be included in the solution which can perform some of the training and scoring of the features.