ML Model Versioning via Automated Snapshots
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Solution Overview
Problem
Manual workflow for machine learning model development is cumbersome, making it difficult to track changes, maintain versions, and deploy models seamlessly across distributed networks.
Innovation Solution
A system and method for building, tracking, and deploying machine learning models through a reproducible processing system that generates snapshots of models, allowing for version control and scalable deployment on distributed networks using a graphical user interface and command-line interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual workflow is used for machine learning model development, then flexibility in model creation is maintained, but tracking changes and maintaining versions becomes cumbersome
Solution Approach 1:
The patent implements automated copying of model states by creating snapshots that capture the entire model environment including code, data, and configuration at specific points in time. This allows reliable version tracking without manual intervention, as each snapshot is an exact copy of the model state at that moment.
Solution Approach 2:
The system performs preliminary actions by automatically capturing and storing model snapshots before changes are made. This proactive approach to version control eliminates the need for manual tracking after changes occur, making the workflow easier while improving reliability.
2Adaptability or versatility
If standard technologies are used for model deployment, then compatibility is maintained, but seamless horizontal scalability across distributed networks is achieved
Solution Approach 1:
The patent creates a universal snapshot format that can be deployed across different devices and network configurations. The snapshot encapsulates all necessary model information in a standardized form that can be universally applied, enabling seamless horizontal scalability without increasing system complexity.
Solution Approach 2:
The model deployment system is segmented into independent snapshots that can be individually managed and distributed. Each snapshot is a self-contained unit that can be deployed independently across distributed networks, facilitating scalability while keeping individual deployment units simple.
3Productivity
If multiple versions of machine learning models are trained and maintained, then model improvement and iteration are enabled, but manual tracking and sharing of versions becomes difficult
Solution Approach 1:
The system implements self-service automation where snapshots automatically track and manage model versions without human intervention. The automated version management system handles tracking, storage, and sharing of multiple model versions, enabling rapid iteration while eliminating time spent on manual version management.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically monitor model training progress and trigger snapshot creation at appropriate milestones. This automated feedback loop ensures that version improvements are captured and managed efficiently, increasing productivity while reducing time spent on manual version control.
Data Source
AI summary
In general, certain embodiments of the present disclosure provide methods and systems for enabling a reproducible processing of machine learning models and scalable deployment on a distributed network. The method comprises building a machine learning model; training the machine learning model to produce a plurality of versions of the machine learning model; tracking the plurality of versions of the machine learning model to produce a change facilitator tool; sharing the change facilitator tool to one or more devices such that each device can reproduce the plurality of versions of the machine learning model; and generating a deployable version of the machine learning model through repeated training.