Machine Learning Platform Dynamic Model Updating
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Solution Overview
Problem
Existing machine learning models often become outdated or inaccurate due to changes in data statistical properties over time, leading to suboptimal performance in applications such as time series forecasting.
Innovation Solution
A machine learning platform that generates and optimizes machine learning models by preparing user-defined tasks and training data, augmenting with external datasets, and continuously monitoring model performance. The platform automatically updates model parameters and redefines tasks as needed to maintain accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained on historical data, then the model can make predictions, but the model becomes outdated and inaccurate when data statistical properties change over time
Solution Approach 1:
The patent implements dynamic model updating by continuously monitoring performance metrics and automatically retraining the machine learning model when data statistical properties change. This transforms the static model into a dynamic system that adapts to changing conditions, resolving the contradiction between initial model accuracy and subsequent obsolescence.
Solution Approach 2:
The system establishes a feedback loop where model performance is continuously evaluated against changing data distributions. When performance degradation is detected, the system triggers automatic retraining with updated data, creating a closed-loop control mechanism that maintains model reliability over time.
2Reliability
If machine learning models are continuously updated to maintain accuracy, then model relevance is improved, but computational resources and time are consumed
Solution Approach 1:
The system monitors changes in data statistical parameters (mean, variance, distribution characteristics) and only triggers model retraining when these parameters exceed predefined thresholds. This selective updating approach maintains model accuracy while avoiding unnecessary computational resource consumption during stable periods.
Solution Approach 2:
The machine learning model updates itself automatically based on performance monitoring without requiring manual intervention. The system self-diagnoses when retraining is needed and self-executes the update process, reducing the need for continuous human oversight and associated computational overhead.
Data Source
AI summary
A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.