Dynamic Predictive Engine Model Selection
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Solution Overview
Problem
Existing machine learning engines are inefficient in responding to environmental changes due to their static nature, relying on infrequent model training and lacking the ability to learn from previous mistakes, which leads to delayed adaptation and reduced relevance in dynamic environments.
Innovation Solution
The proposed machine learning engine dynamically selects models based on current states defined by live data, employs probabilistic weights for ensembling prediction results, and incorporates a feedback mechanism using rewards and penalties to optimize model performance, enabling rapid adaptation to environmental changes and continuous learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If models are trained infrequently in traditional ML engines, then computational resources are conserved, but the models fail to capture changes in the current environment leading to reduced prediction accuracy
Solution Approach 1:
The system transitions from static periodic model training to dynamic continuous model training. Models are continuously retrained in the background as new data becomes available, allowing the system to adapt to environmental changes in real-time while maintaining prediction accuracy without unnecessary delays.
Solution Approach 2:
Model training operates continuously in the background rather than in periodic batches. The system maintains a continuous learning process where models are constantly updated with new data, ensuring uninterrupted adaptation to changing environments while the predictive engine serves predictions using the latest trained models.
2Adaptability or versatility
If multiple models are maintained and evaluated dynamically, then the system can adapt to environmental changes, but computational complexity and resource consumption increase
Solution Approach 1:
The system segments the model ensemble into multiple individual models, each specialized for specific states or conditions. By dividing the prediction task across multiple focused models rather than one complex universal model, the system achieves better adaptability while managing complexity through modular organization of specialized models.
Solution Approach 2:
The system implements feedback mechanisms where prediction results and actual outcomes are continuously evaluated. This feedback drives automatic model selection and retraining processes, allowing the system to adapt to environmental changes automatically without requiring complex manual intervention or oversight.
3Measurement precision
If the system evaluates and retunes models frequently, then prediction accuracy improves, but processing time and computational overhead increase
Solution Approach 1:
Model training is performed preliminarily in the background before predictions are needed. Models are continuously pre-trained and updated asynchronously, so when prediction requests arrive, the system can immediately use the latest trained models without waiting for training to complete, thus maintaining both accuracy and speed.
Solution Approach 2:
The system introduces an intermediary model selection layer that sits between the multiple trained models and the prediction output. This intermediary automatically selects the most appropriate model based on current data characteristics, eliminating the need for complex real-time evaluation of all models and maintaining fast prediction serving.
Data Source
AI summary
The present disclosure relates to methods and devices for operating predictive engines. The present disclosure primarily includes the following features: generating two or more models with different engine structures and parameter sets; generating two or more states according to data and features; deploying the models or part of the models to the states; selecting a top-ranked model in each state; deploying the selected models by states to a live engine; determining a probabilistic weight for each state according to live data and features; ensembling a plurality of prediction results of the models for the states using respective probabilistic weights; and serving the ensembled prediction results as an output of the predictive engine. A machine learning engine responding rapidly, accurately and efficiently to environmental changes is thereby provided.