Automated Ensemble Learning System for Parallel Model Training
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Solution Overview
Problem
Current machine learning techniques require significant user intervention and lack automation in the simultaneous application of data sampling, feature engineering, and model learning processes, making data analysis time-consuming and expensive.
Innovation Solution
An automated end-to-end modeling system that includes data sampling, feature engineering, action labeling, and model learning, allowing for minimal human intervention and simultaneous processing of input data variables to build and update learning models in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated end-to-end modeling system is implemented, then productivity and automation extent are improved, but device complexity increases
Solution Approach 1:
The automated end-to-end modeling system is divided into distinct modular components including data sampling module, feature engineering module, action labeling module, and model learning module. Each module performs a specific function in the machine learning workflow, allowing independent development, testing, and maintenance while achieving high-level automation across the entire data analysis process.
2Productivity
If multiple prediction structures are trained in parallel, then productivity is improved, but device complexity increases
Solution Approach 1:
The system combines multiple prediction structure training processes into a unified parallel processing framework. Multiple models are trained simultaneously on different data subsets or for different tasks within the same automated pipeline, sharing common infrastructure for data sampling, feature engineering, and action labeling, thereby achieving accelerated model development without proportionally increasing overall system complexity.
Data Source
AI summary
A complete end-to-end modeling system is provided that includes data sampling, feature engineering, action labeling, and model learning or learning from models built based on collected data. The end-to-end modeling process is performed via an automatic mechanism with minimal or reduced human intervention. A processor-readable medium is disclosed, storing processor-executable instructions to instantiate an automated data sampling and prediction structure training component, the automated data sampling and prediction structure training component being configured to automatically collect user event data samples, and use the collected user event data samples to train multiple prediction structures in parallel.