fiddler-labs/fiddler-examples
Dec 2024 – Mar 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Computer VisionData Drift DetectionData ScienceFiddlerFiddler ClientMLOps
Josh Rubin
PROFILE
Josh Rubin
Contributed to the fiddler-labs/fiddler-examples repository by developing and enhancing Jupyter notebooks that demonstrate computer vision monitoring and safety evaluation workflows. Leveraged Python and PyTorch to refresh image assets, correct image transformation logic, and improve UMAP visualizations for drift detection, resulting in more accurate and reliable monitoring demonstrations. Built end-to-end, code-first notebooks integrating Fiddler Guardrails safety APIs, enabling users to assess toxicity and content safety with clear visualizations and reproducible workflows. Focused on accelerating onboarding and supporting customer evaluation by providing comprehensive setup guidance and detailed explanations, while ensuring that all solutions were robust, maintainable, and aligned with MLOps best practices.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
4
Lines of code
3,530
Activity Months2
Your Network
32 peopleShared Repositories
16
Work History
March 2025
3 Commits • 2 Features
Mar 1, 2025In March 2025, the fiddler-examples repository focused on delivering end-to-end, customer-ready notebooks that demonstrate how to evaluate Fiddler Guardrails safety capabilities. Two new notebooks provide a hands-on, code-first workflow for assessing toxicity, unethical content, and illegal content via the safety API, with clear setup and visualization to support quick customer onboarding and evaluation. The effort enhances reproducibility, accelerates time-to-value for trial users, and strengthens the reference material for safety workflows.
3 Commits • 2 Features
Mar 1, 2025In March 2025, the fiddler-examples repository focused on delivering end-to-end, customer-ready notebooks that demonstrate how to evaluate Fiddler Guardrails safety capabilities. Two new notebooks provide a hands-on, code-first workflow for assessing toxicity, unethical content, and illegal content via the safety API, with clear setup and visualization to support quick customer onboarding and evaluation. The effort enhances reproducibility, accelerates time-to-value for trial users, and strengthens the reference material for safety workflows.
March 2025
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for fiddler-labs/fiddler-examples: Delivered refreshed CV monitoring image assets, fixed image transformation logic to ensure correct visuals, and enhanced notebooks with improved UMAP visualizations and drift-detection workflows. These efforts improved visual accuracy for demonstrations, strengthened monitoring reliability, and accelerated onboarding for stakeholders and customers.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for fiddler-labs/fiddler-examples: Delivered refreshed CV monitoring image assets, fixed image transformation logic to ensure correct visuals, and enhanced notebooks with improved UMAP visualizations and drift-detection workflows. These efforts improved visual accuracy for demonstrations, strengthened monitoring reliability, and accelerated onboarding for stakeholders and customers.
Activity
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Quality Metrics
Correctness91.2%
Maintainability90.0%
Architecture90.0%
Performance85.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API IntegrationComputer VisionData AnalysisData Drift DetectionData ScienceData VisualizationFiddlerFiddler ClientJupyter NotebookMLOpsMachine LearningModel MonitoringPyTorchPython
Repositories Contributed To
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