ABrain-One/nn-dataset
Oct 2025 – Mar 2026
3 Months active
Languages Used
PythonSQLJSON
Technical Skills
Backend DevelopmentConfiguration ManagementData EngineeringDatabase ManagementPythonSQL
mirzasaifudin
PROFILE
Mirzasaifudin
Worked on the ABrain-One/nn-dataset repository, delivering a series of backend and analytics enhancements over three months. Focused on improving data quality and observability, the work included overhauling the analytics backend, introducing dedicated tables for TFLite and pruning analytics, and reorganizing image classification datasets for better accessibility. Leveraged Python, SQL, and Pandas to implement new API endpoints, streamline data extraction, and align analytics queries with updated data models. Addressed technical debt by removing legacy ingestion paths and outdated configurations, resulting in more reliable performance metrics and operational dashboards. The changes supported faster insights and reduced ongoing maintenance for analytics workflows.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
4
Lines of code
19,173,748
Activity Months3
Your Network
58 peopleShared Repositories
58
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ahsan89-ossMember
ABrain-OneMember
ABrain-OneMember
ABrain-OneMember
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ABrain-OneMember
Work History
March 2026
9 Commits • 2 Features
Mar 1, 2026March 2026: Delivered major analytics backend overhaul and dataset structure improvements in ABrain-One/nn-dataset. Reworked analytics data model, added dedicated tables for TFLite analytics and pruning analytics, removed legacy ingestion paths, expanded API coverage to aggregate runtime, tflite, and pruning analytics, and enhanced statistics exposure. Reorganized image classification dataset structure with renamed JSON files for easier management and accessibility. These changes modernize analytics, enable faster insights, and reduce ongoing maintenance.
9 Commits • 2 Features
Mar 1, 2026March 2026: Delivered major analytics backend overhaul and dataset structure improvements in ABrain-One/nn-dataset. Reworked analytics data model, added dedicated tables for TFLite analytics and pruning analytics, removed legacy ingestion paths, expanded API coverage to aggregate runtime, tflite, and pruning analytics, and enhanced statistics exposure. Reorganized image classification dataset structure with renamed JSON files for easier management and accessibility. These changes modernize analytics, enable faster insights, and reduce ongoing maintenance.
March 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026Summary for 2026-01: Delivered Analytics Configuration Update for Models and Devices in ABrain-One/nn-dataset, improving data fidelity and performance metrics in the run analytics. Consolidated analytics configurations by adding new JSON files for various models and devices and removing outdated entries. Updated core data access by aligning read/write queries with the new analytics structure. This work lays groundwork for more reliable model/device telemetry and supports dashboards with accurate configuration-level metrics.
January 2026
1 Commits • 1 Features
Jan 1, 2026Summary for 2026-01: Delivered Analytics Configuration Update for Models and Devices in ABrain-One/nn-dataset, improving data fidelity and performance metrics in the run analytics. Consolidated analytics configurations by adding new JSON files for various models and devices and removing outdated entries. Updated core data access by aligning read/write queries with the new analytics structure. This work lays groundwork for more reliable model/device telemetry and supports dashboards with accurate configuration-level metrics.
October 2025
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered data-layer improvements in ABrain-One/nn-dataset, focusing on correctness of training statistics and new mobile analytics capabilities. These changes improve data quality, observability, and business insight potential.
2 Commits • 1 Features
Oct 1, 2025Monthly summary for 2025-10: Delivered data-layer improvements in ABrain-One/nn-dataset, focusing on correctness of training statistics and new mobile analytics capabilities. These changes improve data quality, observability, and business insight potential.
October 2025
Activity
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Quality Metrics
Correctness94.2%
Maintainability86.6%
Architecture89.2%
Performance86.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
JSONPythonSQL
Technical Skills
API DevelopmentAPI developmentBackend DevelopmentConfiguration ManagementData AnalysisData EngineeringDatabase ManagementPandasPythonPython programmingSQLdata analysisdata analyticsdata extractiondata organization
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