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
Taha developed a Performance Metrics Retrieval Notebook for the fiddler-labs/fiddler-examples repository, focusing on streamlining the evaluation of binary classification models. Using Python scripting and Jupyter Notebooks, Taha integrated with the Fiddler API to automate the retrieval and processing of key metrics such as accuracy, precision, recall, F1, and AUC. The notebook features robust error handling and exports results to CSV for downstream analysis, supporting reproducible and efficient model assessment. This end-to-end utility enables data scientists to compare experiments more easily and accelerates analytics workflows, demonstrating depth in API integration, data processing, and practical application of data analysis techniques.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
335
Activity Months1
Your Network
28 people
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
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