ML-TANGO/TANGO
Sep 2025 – Dec 2025
3 Months active
Languages Used
JavaScriptPythonShellVueYAML
Technical Skills
API DevelopmentAPI IntegrationBackend DevelopmentConfiguration ManagementContainerizationDocker
박경신
PROFILE
박경신
Gyeongseok Park developed and enhanced containerized workflows for segmentation and continual learning in the ML-TANGO/TANGO repository, focusing on the AutoNN_CL project type. Over three months, he introduced a Docker-based service with YAML validation, automated file generation, and robust status and log handling to streamline workflow setup and testing. He expanded project management features in Vue.js, adding a dedicated status tab and new configuration options to improve tracking and onboarding. Park also strengthened validation and error handling using Python, ensuring clearer diagnostics and stricter dataset checks. His work addressed workflow reliability, reduced runtime failures, and improved developer experience for downstream teams.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
304,441
Activity Months3
Your Network
12 peopleSame Organization
@teslasystem.co.kr1
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Focused on strengthening reliability and robustness of Auto NN CL project workflows in ML-TANGO/TANGO. Implemented enhanced validation and error handling for Auto NN CL project types (Segmentation + Continual Learning), with clearer error messages and stricter dataset checks. This work reduces runtime failures and speeds up issue diagnosis for downstream teams.
1 Commits • 1 Features
Dec 1, 2025December 2025: Focused on strengthening reliability and robustness of Auto NN CL project workflows in ML-TANGO/TANGO. Implemented enhanced validation and error handling for Auto NN CL project types (Segmentation + Continual Learning), with clearer error messages and stricter dataset checks. This work reduces runtime failures and speeds up issue diagnosis for downstream teams.
December 2025
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for ML-TANGO/TANGO: Implemented Auto NN CL project management and configuration enhancements including a new Auto NN CL Status tab in the project manager dashboard, and Segmentation as a new task type in the configuration tab. Standardized default target for Auto NN CL projects from 9 to 5 (PC), improving consistency and reducing misconfigurations. These changes improve project tracking, onboarding for Auto NN CL initiatives, and alignment with downstream workflows.
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for ML-TANGO/TANGO: Implemented Auto NN CL project management and configuration enhancements including a new Auto NN CL Status tab in the project manager dashboard, and Segmentation as a new task type in the configuration tab. Standardized default target for Auto NN CL projects from 9 to 5 (PC), improving consistency and reducing misconfigurations. These changes improve project tracking, onboarding for Auto NN CL initiatives, and alignment with downstream workflows.
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ML-TANGO/TANGO focused on delivering containerized support for segmentation and continual learning workflows. Key work includes introducing the autonn_cl Docker service, YAML validation rules, and automation for file generation, along with improved container status/log handling to enable reliable workflow testing. The autonn_cl container basic structure was implemented and tested for API communication with the project manager.
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ML-TANGO/TANGO focused on delivering containerized support for segmentation and continual learning workflows. Key work includes introducing the autonn_cl Docker service, YAML validation rules, and automation for file generation, along with improved container status/log handling to enable reliable workflow testing. The autonn_cl container basic structure was implemented and tested for API communication with the project manager.
September 2025
Activity
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Quality Metrics
Correctness84.0%
Maintainability84.0%
Architecture84.0%
Performance76.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
JavaScriptPythonShellVueYAML
Technical Skills
API DevelopmentAPI IntegrationAPI developmentBackend DevelopmentConfiguration ManagementContainerizationDockerFront End DevelopmentFrontend DevelopmentMachine Learning OperationsPythonVueVue.jsfull stack development
Repositories Contributed To
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