
Kashkin developed and enhanced evaluation frameworks for generative AI models in the GoogleCloudPlatform/applied-ai-engineering-samples repository over a three-month period. He built Jupyter notebooks and Python scripts to demonstrate spatial reasoning with Gemini 2.0, and implemented robust evaluation recipes for text classification, document question answering, and summarization. Leveraging Python, Vertex AI SDK, and Promptfoo, Kashkin enabled head-to-head benchmarking of model versions and image generation models, supporting upgrade decisions and production readiness. His work emphasized reproducible workflows, comprehensive documentation, and dataset management, resulting in technically sound, maintainable assets that improved model assessment processes without introducing bugs or regressions during development.

Concise monthly summary for May 2025 highlighting key features delivered, major fixes, and impact. Emphasis on business value and technical achievements from the GoogleCloudPlatform/applied-ai-engineering-samples repo.
Concise monthly summary for May 2025 highlighting key features delivered, major fixes, and impact. Emphasis on business value and technical achievements from the GoogleCloudPlatform/applied-ai-engineering-samples repo.
Delivered a focused set of evaluation features in GoogleCloudPlatform/applied-ai-engineering-samples for April 2025, enabling robust benchmarking across upgrades and model-version differences. Implemented three key evaluation feature lines with associated documentation updates, establishing repeatable workflows for model assessment and upgrade decisions.
Delivered a focused set of evaluation features in GoogleCloudPlatform/applied-ai-engineering-samples for April 2025, enabling robust benchmarking across upgrades and model-version differences. Implemented three key evaluation feature lines with associated documentation updates, establishing repeatable workflows for model assessment and upgrade decisions.
Monthly overview for 2024-12: Focused on delivering a demo feature and repo hygiene that demonstrates Gemini 2.0 spatial reasoning capabilities via Vertex AI SDK. The main deliverable is a Gemini 2.0 Spatial Reasoning Demo Notebook in the GoogleCloudPlatform/applied-ai-engineering-samples repository, with creation and updates of the notebook, prompt refinements, and attribution link updates to ensure accurate references. No major bugs were recorded for this month based on the provided data; however, notebook housekeeping and demo readiness were completed to support customer evaluations.
Monthly overview for 2024-12: Focused on delivering a demo feature and repo hygiene that demonstrates Gemini 2.0 spatial reasoning capabilities via Vertex AI SDK. The main deliverable is a Gemini 2.0 Spatial Reasoning Demo Notebook in the GoogleCloudPlatform/applied-ai-engineering-samples repository, with creation and updates of the notebook, prompt refinements, and attribution link updates to ensure accurate references. No major bugs were recorded for this month based on the provided data; however, notebook housekeeping and demo readiness were completed to support customer evaluations.
Overview of all repositories you've contributed to across your timeline