Automated ML Deployment Validation and Simulation

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Solution Overview

Problem

Current automated tools lack the ability to determine machine learning model requirements for production environments, validate model execution, and deploy models efficiently, leading to resource inefficiencies and deployment failures, especially in non-standard environments.

Innovation Solution

A method that identifies resource and code module usage specifications, prunes unnecessary components, selects a suitable deployment environment, validates resources and code modules, simulates model execution, and deploys the model only if successful, ensuring optimal resource allocation and accurate deployment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated deployment tools are used, then deployment speed is improved, but deployment reliability deteriorates due to inability to validate model execution

Engineering Contradiction:

Improvedeployment speedVSAvoiddeployment reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary validation of the target execution environment by checking resource availability and code module presence before actual model deployment. This advance verification ensures that deployment failures are prevented, maintaining both high deployment speed and reliability.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms that monitor model execution validation results and use this information to adjust deployment decisions. This closed-loop approach ensures that only models validated for successful execution are deployed, improving reliability without sacrificing deployment efficiency.

Inventive Principle:

Principle #23Feedback

2Manufacturing precision

If comprehensive resource validation is performed, then deployment accuracy is improved, but deployment time increases

Engineering Contradiction:

Improvedeployment accuracyVSAvoiddeployment time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The validation process is segmented into distinct checks: resource validation, code module validation, and execution validation. This segmentation allows the system to perform comprehensive validation thoroughly while maintaining efficient deployment by processing checks in an organized sequence and avoiding redundant operations.

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If manual deployment processes are used, then deployment control is improved, but resource efficiency deteriorates due to over-provisioning

Engineering Contradiction:

Improvedeployment controlVSAvoidresource efficiency

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system performs self-service by automatically identifying resource requirements, validating the execution environment, and deploying models without manual intervention. This automation maintains precise control over resource allocation while eliminating the over-provisioning that occurs with manual deployment processes, thereby improving resource efficiency.

Inventive Principle:

Principle #25Self-service

4Adaptability or versatility

If automated environment configuration is performed, then deployment standardization is improved, but adaptability to non-standard environments deteriorates

Engineering Contradiction:

Improveenvironment adaptabilityVSAvoiddeployment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system adapts to different execution environments by dynamically adjusting validation parameters and checks based on the specific environment characteristics. This allows standardized deployment processes to be applied flexibly across diverse environments, maintaining both standardization and adaptability without excessive complexity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12118340B2Automated machine learning model deployment

Publication Date: 2024.10.15 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Using exported data of a machine learning model and a model training environment specification, a resource usage specification and a code module usage specification of the model are identified. A code module installation specification is determined from a code module requirements specification and a target execution environment specification. The code modules specified by the code module installation specification are caused to be installed in the target execution environment. Using data of the updated target execution environment, the updated target execution environment is validated for execution of the model. Execution of the model in the updated target execution environment is simulated. The model is deployed in the updated target execution environment responsive to the simulating being successful.