
Tuan Anh contributed to the ISE-UET-AutoML/frontend repository by building and refining complex data labeling, dataset management, and model training workflows. He integrated features such as Label Studio-based annotation, object detection with canvas drawing, and secure dataset uploads using AWS S3 pre-signed URLs. His work included UI/UX enhancements, robust authentication with secure cookies, and real-time prediction interfaces, all implemented with React, JavaScript, and Ant Design. By focusing on component-based architecture and state management, Tuan Anh improved workflow efficiency, data integrity, and deployment stability, demonstrating depth in frontend engineering and delivering production-ready solutions for machine learning data operations.

Concise monthly summary for 2025-07 highlighting delivered features, major bug fixes, impact, and technical accomplishments across the ISE-UET-AutoML/frontend repo. Emphasis on business value, end-user improvements, and engineering excellence.
Concise monthly summary for 2025-07 highlighting delivered features, major bug fixes, impact, and technical accomplishments across the ISE-UET-AutoML/frontend repo. Emphasis on business value, end-user improvements, and engineering excellence.
June 2025 monthly summary for ISE-UET-AutoML/frontend: Delivered major front-end enhancements to labeling and dataset ingestion, improving workflow efficiency, data integrity, and deployment stability.
June 2025 monthly summary for ISE-UET-AutoML/frontend: Delivered major front-end enhancements to labeling and dataset ingestion, improving workflow efficiency, data integrity, and deployment stability.
April 2025 (ISE-UET-AutoML/frontend): Delivered a data labeling capability by integrating Label Studio and hardened authentication cookie security, enhancing both data preparation workflows and security posture in the frontend. The work enables in-app labeling, safer session handling, and aligns with best practices for credential protection.
April 2025 (ISE-UET-AutoML/frontend): Delivered a data labeling capability by integrating Label Studio and hardened authentication cookie security, enhancing both data preparation workflows and security posture in the frontend. The work enables in-app labeling, safer session handling, and aligns with best practices for credential protection.
Summary for 2025-03: Delivered a cohesive UI overhaul and data-management enhancements across the ISE-UET-AutoML/frontend, enabling faster project setup, clearer training results, and more reliable deployment workflows. Implemented real-time prediction capabilities, expanded multi-label data handling, and improved navigation and data analysis UX. Strengthened reliability through robust error handling and observability, delivering tangible business value with faster model iteration, improved data governance, and clearer feedback for users.
Summary for 2025-03: Delivered a cohesive UI overhaul and data-management enhancements across the ISE-UET-AutoML/frontend, enabling faster project setup, clearer training results, and more reliable deployment workflows. Implemented real-time prediction capabilities, expanded multi-label data handling, and improved navigation and data analysis UX. Strengthened reliability through robust error handling and observability, delivering tangible business value with faster model iteration, improved data governance, and clearer feedback for users.
February 2025 frontend delivery accelerated model training workflows and dataset management for ISE-UET-AutoML/frontend. Key UI/features and backend readiness matured multimodal pipelines, introduced background training jobs, and tidied release readiness through branch synchronization and UI refinements.
February 2025 frontend delivery accelerated model training workflows and dataset management for ISE-UET-AutoML/frontend. Key UI/features and backend readiness matured multimodal pipelines, introduced background training jobs, and tidied release readiness through branch synchronization and UI refinements.
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