ML Resource Allocation Using Changepoint-Guided Model Execution
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Solution Overview
Problem
Existing machine learning models often execute on inconsistent or erroneous data, leading to wasted computational resources and increased latency due to the need for retraining or rebuilding, as errors are typically detected only after execution.
Innovation Solution
Implement a system that detects changepoints in production data and adjusts the execution of machine learning models based on the performance scores of other models, optimizing resource allocation by selectively executing or rebuilding models to prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models execute on data without prior validation, then processing speed is maintained, but computational resources are wasted and errors are detected late
Solution Approach 1:
The system performs preliminary validation by detecting changepoints in data before executing machine learning models. This advance detection prevents models from running on erroneous data, eliminating wasted computational resources while maintaining processing speed through selective execution only when data quality is confirmed.
Solution Approach 2:
The system implements feedback mechanisms where performance scores from model executions are monitored and fed back into the system. When performance degradation is detected, the system triggers changepoint detection and adjusts model execution accordingly, creating a closed-loop system that prevents resource waste while maintaining productivity.
2Adaptability or versatility
If all machine learning models execute on every data batch, then model coverage is maximized, but resource allocation becomes inefficient
Solution Approach 1:
The system applies local quality by selectively executing models based on local data characteristics. Instead of uniformly executing all models on every data batch, the system detects changepoints and performance scores to determine which models should execute on specific data, optimizing resource allocation while maintaining comprehensive model coverage where needed.
Solution Approach 2:
The system implements dynamic resource allocation where model execution is adjusted in real-time based on detected changepoints and performance scores. The system dynamically determines which models execute on which data batches, transitioning from static universal execution to dynamic selective execution that maximizes both coverage and efficiency.
3Reliability
If models are rebuilt or retrained after errors are detected, then model accuracy is restored, but latency increases due to wasted time
Solution Approach 1:
The system performs preliminary changepoint detection and performance validation before model execution. By detecting data quality issues in advance and preventing model execution on erroneous data, the system eliminates the need for post-error rebuilding and retraining, thereby maintaining both high reliability and low latency.
Solution Approach 2:
The system implements a skip mechanism where models are selectively executed only when data quality is confirmed. When changepoints indicate poor data quality, the system skips model execution entirely rather than proceeding with execution that would fail, thereby avoiding the time-consuming processes of error detection, model rebuilding, and retraining.
Data Source
AI summary
Some embodiments of the present application include obtaining first data from a data feed to be provided to a plurality of machine learning models and detecting a changepoint in the first data. In response to the changepoint being detected, a first machine learning model may be executed on the first data to obtain first output datasets. A first performance score for the first machine learning model may be computed based on the first output datasets. A second machine learning model may be caused to execute on the first data based on the first performance score satisfying a first condition.