Autonomous Algorithm Evaluation via Shadow Environment
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Solution Overview
Problem
The existing methods for evaluating 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 efficient and accurate performance management that respects confidentiality and privacy concerns.
Innovation Solution
A system that includes an algorithm data store and a deployment platform with a lifecycle manager and performance manager, which manages the execution of current and potential replacement algorithms in live and shadow environments, respectively, using source data to report performance information and facilitate seamless replacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing of new algorithms is performed to evaluate performance, then algorithm performance can be improved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system creates a shadow environment that copies the live production environment, allowing algorithms to be tested on replicated data without affecting actual operations. This enables parallel evaluation of multiple algorithm versions simultaneously, dramatically reducing the time required for performance assessment while maintaining evaluation accuracy.
Solution Approach 2:
The system performs preliminary testing of new algorithms in the shadow environment before deploying them to the live environment. By evaluating algorithms upfront on replicated data streams and comparing their performance against the current algorithm, the system identifies improvements before they impact production, streamlining the evaluation process and reducing overall deployment time.
2Reliability
If manual testing of new algorithms is performed to evaluate performance, then algorithm performance can be improved, but the process becomes error-prone
Solution Approach 1:
The system implements automated feedback loops that continuously monitor and compare the performance of the current algorithm against potential replacement algorithms in the shadow environment. Performance metrics are automatically collected, analyzed, and used to determine whether replacements should be made, eliminating manual errors and ensuring consistent, accurate evaluation criteria are applied throughout the testing process.
Solution Approach 2:
The performance manager autonomously evaluates algorithms, compares their performance, and makes replacement decisions without human intervention. The system self-manages the entire evaluation process including data replication, algorithm execution, performance measurement, and deployment decisions, thereby eliminating human errors associated with manual testing while maintaining high measurement precision.
3Measurement precision
If algorithm evaluation is performed in the live environment, then real performance data can be obtained, but confidentiality and security concerns arise
Solution Approach 1:
The system segments the evaluation process into two distinct environments: the live production environment that maintains confidentiality by processing only real data, and the shadow testing environment that uses replicated data for algorithm evaluation. This segmentation allows performance testing without exposing sensitive production data to potential security risks while still obtaining accurate performance measurements.
Solution Approach 2:
The shadow environment acts as an intermediary between the live environment and the evaluation process. It receives replicated data from the live environment, performs algorithm testing and comparison, and feeds results back to the performance manager without ever handling the actual confidential production data. This intermediary layer enables accurate performance measurement while protecting sensitive information from exposure.
Data Source
AI summary
Methods and apparatus disclosed herein autonomously evaluate replacement algorithms. An example system associated with a live environment executing a current algorithm includes at least one memory, instructions, and processor circuitry to execute the instructions to manage execution of the current algorithm in the live environment, the live environment associated with a deployment platform, and manage, based on receipt of at least one potential replacement algorithm, a shadow environment causes execution of the at least one potential replacement algorithm in the shadow environment, the shadow environment instantiated separately from the live environment, the shadow environment utilizes data from the live environment during execution of the at least one potential replacement algorithm in the shadow environment.