Bob Giezi developed end-to-end Jupyter notebooks for the IBM/terratorch repository, focusing on generating embedding vectors from Sentinel-2 RGB imagery using the TerraMind deep learning model. He designed reproducible workflows that integrate geospatial data with machine learning, enabling rapid experimentation and smoother onboarding for new users. His work included model setup, image processing, and embedding retrieval, all implemented in Python and leveraging PyTorch for deep learning tasks. By providing ready-to-run templates, Bob addressed the need for practical, business-oriented demonstrations, supporting client engagements and accelerating geospatial deep learning adoption. The work demonstrated solid depth in geospatial analysis and workflow design.