Online Machine Learning Pipeline for Drift-Stable Continuous Learning
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Solution Overview
Problem
Current machine learning systems face challenges in maintaining stability and robustness, especially in dynamic environments like banking, insurance, and health, where models need to continuously adapt to changing data and be accountable for their decisions.
Innovation Solution
The development of a Continuously Learning, Stable, and Robust Online Machine Learning System (OMLS) that can learn concurrently from both streaming data and human-provided data, incorporating features like Online Machine Learning Engine (OMLE), Online Preprocessing Engine (OPrE), Online Feature Engineering Engine (OFEE), and Online Robust Feature Selection Engine (ORFSE) to ensure model stability and understandability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained in batch mode on available datasets, then the models can achieve high accuracy on training data, but the models become overfitted and fail to adapt to changing data distributions in dynamic environments
Solution Approach 1:
The patent implements a dynamic online learning system where the machine learning model continuously updates its parameters as new data arrives, rather than being static after batch training. The system processes data streams in real-time and adapts to changing data distributions through continuous retraining and model updates, enabling the model to maintain accuracy in dynamic environments.
Solution Approach 2:
The patent establishes a continuous learning process where the model is constantly trained on incoming data streams without interruption. The system maintains a continuous feedback loop between data ingestion, model training, evaluation, and deployment, ensuring the model continuously improves and adapts rather than undergoing periodic batch updates.
2Adaptability or versatility
If machine learning models continuously adapt to changing data in real-time, then the models maintain high performance in dynamic environments, but the models become unstable and difficult to operate
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors model performance metrics, data distribution changes, and prediction quality. Based on this feedback, the system automatically adjusts training parameters, triggers retraining events, and manages model versions to maintain stability while adapting to changes. The feedback loop includes performance evaluation, drift detection, and automated model management.
Solution Approach 2:
The patent dynamically adjusts model parameters and hyperparameters based on incoming data characteristics and performance feedback. The system modifies learning rates, batch sizes, and other training parameters in real-time to optimize adaptation while preventing instability. Parameter changes are controlled through structured update rules that balance adaptability with operational stability.
3Productivity
If complex machine learning models are used to handle dynamic data streams, then the models can capture intricate patterns, but the models become black boxes that are difficult to understand and explain
Solution Approach 1:
The patent introduces explanation and interpretation components as intermediaries between the complex machine learning model and users. These components include feature importance analysis, prediction reasoning generators, and model behavior visualizers that translate complex model decisions into human-understandable explanations without altering the underlying model's predictive capabilities.
4Reliability
If machine learning models are periodically retrained in batch mode, then the models can be validated and updated, but the models perform poorly between training periods and it is difficult to know when to retrain
Solution Approach 1:
The patent implements drift detection and performance monitoring mechanisms that proactively identify when data distribution changes or model performance degrades, triggering retraining events before significant performance loss occurs. The system continuously evaluates data characteristics and predicts when retraining will be beneficial, enabling timely model updates rather than relying on fixed schedules.
Data Source
AI summary
An Online Machine Learning System (OMLS) including an Online Preprocessing Engine (OPrE) configured to (a) receive streaming data including an instance comprising a vector of inputs, the vector of inputs comprising a plurality of continuous or categorical features; (b) discretize features; (c) impute missing feature values; (d) normalize features; and (e) detect drift or change in features; an Online Feature Engineering Engine (OFEE) configured to produce features; and an Online Robust Feature Selection Engine (ORFSE) configured to evaluate and select features; an Online Machine Learning Engine (OMLE) configured to incorporate and utilize one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein each of the OMLE, the OPrE, the OFEE, and the ORFSE are continuously communicatively coupled to each other, and wherein the OMLS is configured to perform continuous online machine learning.