Algorithm Selection Platform for Automated Shadow Testing
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Solution Overview
Problem
The existing methods for selecting and replacing algorithms used in industrial assets are time-consuming, expensive, and error-prone, especially when dealing with complex algorithms, and there is a need for a solution that can efficiently and accurately manage algorithm updates while maintaining confidentiality and security concerns.
Innovation Solution
A system comprising an algorithm data store and an algorithm analysis engine that compares available algorithms with requirements of the current algorithm, selects potential replacements, and manages their execution in a shadow environment to report performance information, allowing for automated and secure evaluation and deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing and evaluation of new algorithms is performed to improve algorithm performance, then algorithm performance can be improved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system performs preliminary actions by automatically pre-screening and evaluating multiple algorithms against historical data and performance criteria before deployment. The algorithm analysis engine conducts comprehensive evaluations including accuracy, computational efficiency, and resource requirements in advance, so that when algorithms need to be replaced in production, the selection process is significantly accelerated while maintaining rigorous performance standards.
Solution Approach 2:
The system implements self-service by enabling automated algorithm selection and replacement without requiring extensive manual intervention. The lifecycle manager automatically monitors algorithm performance, triggers reevaluation when performance degradation is detected, and coordinates the replacement process. This automation eliminates time-consuming manual testing and evaluation while ensuring that performance improvements are systematically achieved.
2Reliability
If manual testing and evaluation of new algorithms is performed to improve algorithm performance, then algorithm performance can be improved, but the process becomes expensive
Solution Approach 1:
The system performs automated self-evaluation of algorithms using historical data and performance metrics, eliminating the need for expensive manual testing and expert consultation. The algorithm analysis engine automatically assesses algorithm performance across multiple dimensions including accuracy, computational efficiency, and resource requirements, significantly reducing the cost of algorithm evaluation while maintaining rigorous performance standards.
Solution Approach 2:
The system uses historical data and simulated environments as copies of real-world conditions to evaluate algorithms before deployment. By testing algorithms against historical operational data and performance criteria in a controlled environment, the system can assess algorithm performance without incurring the full cost of extensive field testing and manual evaluation.
3Power
If cloud-based algorithm execution is used to improve computational capabilities, then processing power is enhanced, but sensitive asset information may be exposed
Solution Approach 1:
The system segments the algorithm execution environment into distinct cloud and edge portions. Sensitive asset information is processed locally at the edge, while only non-sensitive data and algorithm computations are transmitted to the cloud. This segmentation allows the system to leverage cloud computational power for algorithm execution while maintaining security by keeping sensitive information localized and isolated from potential cloud-based threats.
4Productivity
If automated algorithm selection and deployment is implemented to reduce time and cost, then operational efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements a universal algorithm selection and deployment platform that handles multiple algorithm types, evaluation criteria, and deployment scenarios through a single integrated system. The algorithm analysis engine can evaluate algorithms across diverse domains and applications using the same automated framework, reducing operational complexity by providing a standardized multi-functional solution rather than requiring separate systems for different algorithm management tasks.
Data Source
AI summary
An algorithm data store may contain information about a pool of available algorithms. An algorithm analysis engine may compare the information about each of the pool of available algorithms with at least one requirement associated with the current algorithm executing in the live environment. Based on the comparison, the algorithm analysis engine may select at least one of the pool of available algorithms as a potential replacement algorithm and transmit an indication of the selected at least one potential replacement algorithm. A deployment platform may include a lifecycle manager that manages execution of the current algorithm in the live environment. The lifecycle manager may also receive the indication of the selected at least one potential replacement algorithm, manage execution of the at least one potential replacement algorithm in a shadow environment, and report performance information associated with the current algorithm and the at least one potential replacement algorithm.