ML Repository Branching and Validation for Parallel Development
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Solution Overview
Problem
Current MLOps systems face challenges in efficient parallel development and collaboration among different teams, with high burdens in tracking and managing ML models, leading to prolonged development times and lack of effective collaboration.
Innovation Solution
A system and method for creating a structured code repository with automated branching and tagging, including a pipeline, configuration, notebooks, steps, and tests folders, with automated validation and tag creation to enforce standards and streamline development processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tracking and management of ML models is used, then developers have flexibility in development process, but development time increases and collaboration efficiency decreases
Solution Approach 1:
The system performs preliminary actions by automatically creating standardized branch structures (main, develop, data-science, machine-learning, quality-assessment) and project templates before development begins. This pre-configuration enables teams to start working immediately without manual setup, resolving the contradiction by eliminating initial setup time while maintaining development flexibility.
Solution Approach 2:
The system implements self-service through automated validation that checks file existence, function containment, and signature correctness without human intervention. This automation resolves the contradiction by eliminating manual tracking overhead while preserving developer flexibility through configurable validation rules.
2Productivity
If standardized project templates and automated validation are implemented, then development efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the development process into distinct standardized branches (data-science for experimentation, machine-learning for model development, quality-assessment for validation) and organized folders (pipeline, configuration, notebooks, steps, tests, utils). This segmentation resolves the contradiction by making complexity manageable through clear separation while improving efficiency through specialized workflows for each segment.
Solution Approach 2:
The system changes parameters by enforcing standardized folder structures, file naming conventions, and validation rules that transform ad-hoc development into structured processes. This parameter standardization resolves the contradiction by reducing the cognitive load of managing complexity while significantly improving development efficiency through consistent patterns.
3Adaptability or versatility
If automated branching and tagging systems are used, then parallel development and collaboration improve, but tracking and management burden increases
Solution Approach 1:
The system implements dynamic branch creation that automatically adapts to different development needs through standardized templates. Teams can create multiple parallel branches following the same structure, enabling flexible collaboration without manual tracking. This resolves the contradiction by making the tracking system adaptive rather than rigid, allowing parallel development while reducing management burden through automation.
Solution Approach 2:
The system provides automated feedback through validation that checks code quality, file structure, and function signatures across branches. This feedback mechanism resolves the contradiction by replacing manual tracking burden with automated quality assurance, enabling parallel development while reducing the cognitive load of model management through continuous validation.
4Reliability
If validation processes check file existence, function containment, and signature correctness, then code quality improves, but development time increases
Solution Approach 1:
The validation process operates continuously through automated workflows that check file existence, function containment, and signature correctness as part of the normal development flow rather than as separate manual steps. This continuity resolves the contradiction by making validation an integrated, automated process that maintains code quality while minimizing perceived development time through seamless execution.
Data Source
AI summary
A system and method may create and manage a code repository structure and related files for a ML system such as a NN by executing code to create a version control management platform template commit including a pipeline folder comprising deploy files; a configuration folder comprising key-value pairs describing variables used as input to the neural network; a notebooks folder; a steps folder including ML or NN code; a tests folder comprising test scripts; and a utils folder; and executing code to create a tag associated with a pull request and alter version control management platform data such that the tag is displayed in conjunction with data describing the branch associated with the pull request. Executing code to create a tag may include searching a commit history for a unique commit number associated with a pull request, and based on this search creating a tag.