Containerized Model Training From Build Artifacts

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Solution Overview

Problem

Existing systems for developing machine learning models are inefficient, requiring excessive computational resources, increased development time, and lack scalable and secure mechanisms for tracking user contributions, particularly in distributed environments.

Innovation Solution

A networked system utilizing container technology provides a development environment that decouples model training from local resources, offers secure process isolation, and facilitates quicker scaling and user tracking through tokens, leveraging a container platform for efficient machine learning model development.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If machine learning models are trained using existing systems, then model training can be performed, but computational resources are excessively consumed and development time increases

Engineering Contradiction:

Improvemodel training efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system segments the model training process into distinct phases (data preparation, model training, evaluation, deployment) and manages each phase separately through automated workflows. This segmentation allows for optimized resource allocation at each stage, preventing excessive computational resource consumption while maintaining training efficiency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by automatically preparing training data, selecting appropriate models, and configuring training parameters before the actual training begins. This preliminary automation reduces the need for manual intervention and optimizes resource usage from the outset, improving productivity while controlling computational resource consumption.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If traditional development environments are used, then model training can proceed, but development time is excessive and scaling is difficult

Engineering Contradiction:

Improvedevelopment speedVSAvoiddevelopment time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system implements self-service capabilities where the platform automatically handles environment setup, dependency installation, and configuration management without requiring manual intervention. This self-service approach significantly reduces development time and allows teams to scale model training operations without proportionally increasing operational overhead.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If distributed development environments are used, then collaboration is enabled, but secure tracking of user contributions is lacking

Engineering Contradiction:

Improvedistributed collaboration capabilityVSAvoidsecurity and tracking reliability

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system implements comprehensive feedback mechanisms that automatically track and record user contributions throughout the model training lifecycle. Each action, code commit, and model change is logged with user attribution, creating a reliable audit trail that maintains security and accountability in distributed collaboration environments while enabling versatile team workflows.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12417092B2Model training using build artifacts

Publication Date: 2025.09.16 STRIPE LLC

AI summary

The subject technology detects a code commit at a code repository. The subject technology sends a request for a build job to a build server. The subject technology determines that the build job is completed. The subject technology sends a training request and user token to a proxy authenticator. The subject technology determines determining that the user token is validated. The subject technology sends a training request and the user token to a training job manager. Further, the subject technology determines determining that the training job is completed.