
Josh contributed to the fiddler-labs/fiddler-examples repository by developing and enhancing Jupyter notebooks that demonstrate computer vision monitoring and safety evaluation workflows. He refreshed and expanded image assets for CV monitoring, corrected image transformation logic to improve visual accuracy, and enhanced UMAP visualizations for drift detection. In addition, Josh built end-to-end Python notebooks integrating Fiddler’s Guardrails safety API, enabling users to assess toxicity and content safety with clear visualizations and reproducible workflows. His work combined skills in Python, data visualization, and machine learning, resulting in more reliable demonstrations, improved onboarding materials, and streamlined evaluation processes for both internal stakeholders and customers.

In 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.
In 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.
December 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 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.
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