Rule-Based ML Model Promotion via Metadata Evaluation
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Solution Overview
Problem
The promotion of machine learning models from a training environment to a production environment is a highly manual process, lacking automation, which hinders scalability and efficiency for enterprise-level organizations.
Innovation Solution
A rule-based promotion method using a rules engine that generates and evaluates metadata files in standardized formats (such as JSON) to determine if a model meets predefined criteria for deployment, allowing or halting the release pipeline based on boolean evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a manual promotion process is used for machine learning models, then model evaluation can be performed with human judgment, but the process lacks scalability and efficiency for enterprise-level organizations
Solution Approach 1:
The patent replaces manual human evaluation with an automated rules engine that executes predefined promotion rules. The system automatically parses model metadata, evaluates it against promotion criteria, and makes deployment decisions without human intervention, thereby achieving scalability and efficiency for enterprise-level model promotion.
Solution Approach 2:
The system enables self-service automation where the rules engine autonomously performs model promotion evaluations. The engine automatically retrieves model metadata, applies promotion rules, and makes decisions without requiring continuous human oversight, allowing the system to serve itself in the model promotion process.
2Reliability
If manual evaluation is used for model promotion, then complex model characteristics can be assessed by experts, but the process becomes time-consuming and less scalable
Solution Approach 1:
The system performs preliminary actions by pre-defining promotion rules and criteria before model deployment. These rules are established in advance and stored in the rules engine, allowing automated evaluation of model metadata against predetermined standards. This eliminates the need for time-consuming ad-hoc expert reviews while maintaining reliable model readiness assurance.
Solution Approach 2:
The patent substitutes manual expert evaluation with an automated rules engine that rapidly assesses model characteristics against predefined criteria. The engine processes model metadata efficiently, providing reliable evaluation decisions much faster than manual processes, thereby reducing promotion process duration without sacrificing reliability.
3Extent of automation
If standardized metadata formats are implemented, then automation can be achieved through consistent data structures, but the system complexity increases due to format requirements
Solution Approach 1:
The patent implements a universal standardized metadata format that serves multiple functions: it stores model information, enables automated parsing by the rules engine, facilitates rule evaluation, and supports deployment decisions. This single standardized format replaces multiple ad-hoc data structures, achieving automation while managing system complexity through a unified approach.
Solution Approach 2:
The standardized metadata format acts as an intermediary between model generation and the rules engine evaluation process. It provides a consistent data structure that the rules engine can reliably parse and process, facilitating automation without requiring direct complex interactions between all system components.
4Productivity
If automated rules evaluation is implemented, then scalability is improved for enterprise-level organizations, but the system requires sophisticated rules engines and evaluation frameworks
Solution Approach 1:
The patent segments the promotion evaluation system into distinct components: a rules engine that manages evaluation logic, a metadata management layer that handles data storage and parsing, and a decision-making framework that executes promotion actions. This segmentation allows each component to be optimized independently, achieving scalability while managing overall system complexity through modular architecture.
Solution Approach 2:
The system manages complexity by changing parameters in a controlled manner - the rules engine operates with standardized metadata inputs and produces consistent evaluation outputs. By parameterizing the evaluation process through predefined rules and criteria, the system achieves scalability without requiring proportional increases in system complexity.
Data Source
AI summary
Systems and methods for rule-based machine learning model promotion are disclosed. In accordance with aspects, a method may include providing a rules engine that defines a software object model, and an evaluation framework. A model metadata file having a format that is based on the software object model can be generated. The model metadata file can store metadata associated with the model. A model rule file having a format based on the software object model and that defines rule criteria for evaluating the metadata can be generated. The rules engine can instantiate a software object based on the software object model and parse the model rule file to determine rule criteria and parse the model metadata file to determine a parameter value associated with the rule criteria. The rules engine can evaluate the parameter value against a rule and provide a promotion decision for the model.