Streaming Machine Learning Platform for Real-Time Anomaly Detection
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Solution Overview
Problem
Traditional machine learning approaches face challenges in deploying high-throughput, always-on systems that can dynamically adjust to input and detect anomalous events in real-time, particularly in handling large-scale network traffic from millions of concurrent users.
Innovation Solution
A streaming machine learning platform processes event data through preprocessing, machine learning models, and post-processing to generate model input and output data, which includes cardinality data for dynamic model selection and real-time anomaly detection, enabling real-time analysis and action on anomalous events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional HTTP-based machine learning models are deployed, then deployment simplicity is maintained, but real-time processing capability and throughput are limited
Solution Approach 1:
The system segments the machine learning processing into multiple independent components: event sources, preprocessing applications, machine learning models, post-processing applications, and publishing applications. Each component operates independently in the event stream, allowing high throughput while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements continuous event stream processing where data flows continuously through preprocessing, machine learning inference, and post-processing stages without interruption. This continuous action enables high throughput and real-time response, overcoming the batch processing limitations of traditional HTTP approaches.
2Reliability
If machine learning models process data in batches, then computational resources are saved, but real-time anomaly detection capability is lost
Solution Approach 1:
The system performs preliminary actions by preprocessing events before they reach the machine learning model, extracting relevant features and preparing data in advance. This reduces the computational burden on the model during real-time inference while maintaining immediate detection capability.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary portions of data through the machine learning model, rather than processing entire batches. The preprocessing and post-processing applications handle data transformation, allowing the model to focus only on critical inference tasks, reducing computational resource consumption while maintaining real-time performance.
3Adaptability or versatility
If static machine learning models are deployed, then deployment simplicity is maintained, but adaptability to dynamic input is limited
Solution Approach 1:
The system introduces dynamics by allowing the machine learning model to receive adaptive input through the preprocessing application, which can transform and prepare data dynamically based on the event stream characteristics. The model responds in real-time to varying input patterns, enabling adaptability without requiring complete system redesign.
Solution Approach 2:
The patent implements feedback mechanisms where the post-processing application analyzes model outputs and can trigger retraining of the machine learning model when performance degradation or concept drift is detected. This feedback loop enables the system to adapt to changing patterns while maintaining a relatively simple core architecture.
4Speed
If high-throughput streaming processing is implemented, then real-time response is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional platform where a single event stream infrastructure handles multiple operations: event collection, preprocessing, machine learning inference, post-processing, and publishing. This universal approach enables high-throughput processing while avoiding the complexity of separate specialized systems for each function.
Data Source
AI summary
A method implements a streaming machine learning platform. The method includes processing event data with a preprocessing application to generate model input data and cardinality data in response to receiving the event data from a streaming application. The method further includes forwarding, using the cardinality data, the model input data to a machine learning model. The method further includes processing the model input data with the machine learning model to generate model output data stored to an output record. The method further includes processing the model output data from the output record with a post processing application to generate post data stored to a post record. The method further includes generating notification data using the post data of the post record and transmitting a notification of the notification data.