fiddler-labs/fiddler-examples
Aug 2025 – Aug 2025
1 Month active
Languages Used
JSONPython
Technical Skills
API IntegrationData AnalysisData ProcessingJupyter NotebooksPython Scripting
Taha Siddiqui
PROFILE
Taha Siddiqui
Developed a Performance Metrics Retrieval Notebook for the fiddler-labs/fiddler-examples repository, enabling streamlined access to binary classification model metrics via the Fiddler API. The solution, implemented in Python and Jupyter Notebooks, automated the retrieval, processing, and export of key metrics such as accuracy, precision, recall, F1, and AUC. By incorporating robust error handling and end-to-end workflow automation, the notebook improved the reliability and reproducibility of model evaluation. The utility facilitated faster cross-experiment comparisons and supported downstream analytics through CSV exports, leveraging skills in API integration, data analysis, and data processing to accelerate data science workflows for teams.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
335
Activity Months1
Your Network
32 peopleShared Repositories
16
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025Delivered a new Performance Metrics Retrieval Notebook for binary classification models, streamlining retrieval and processing of metrics (accuracy, precision, recall, F1, AUC) from the Fiddler API. The notebook covers API configuration, metric gathering, CSV export, and robust error handling, enabling faster, reproducible model evaluation and cross-experiment comparisons. This work accelerates data science cycles and strengthens metrics-driven decision making.
1 Commits • 1 Features
Aug 1, 2025Delivered a new Performance Metrics Retrieval Notebook for binary classification models, streamlining retrieval and processing of metrics (accuracy, precision, recall, F1, AUC) from the Fiddler API. The notebook covers API configuration, metric gathering, CSV export, and robust error handling, enabling faster, reproducible model evaluation and cross-experiment comparisons. This work accelerates data science cycles and strengthens metrics-driven decision making.
August 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONPython
Technical Skills
API IntegrationData AnalysisData ProcessingJupyter NotebooksPython Scripting
Repositories Contributed To
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