ai-solution-eng/ai-solution-demos
Aug 2025 – Oct 2025
2 Months active
Languages Used
DockerfileMarkdownPythonShellYAMLJupyter Notebook
Technical Skills
3D RenderingComputer VisionDockerDocumentationHelmKubernetes
fcaliva
PROFILE
Fcaliva
Contributed to the ai-solution-demos repository by developing a Streamlit-based demo application for interactive blood vessel geometry analysis and 3D reconstruction, integrating NVIDIA Vista-3D segmentation and deploying on HPE Private Cloud AI. Leveraged Python, Docker, and Kubernetes to create an end-to-end analysis pipeline, accompanied by comprehensive deployment documentation and updated READMEs to streamline reproducibility and onboarding. Addressed a critical visualization bug in the demo notebook by correcting import paths, ensuring seamless execution of visualization steps. The work emphasized robust deployment practices, clear documentation, and reliable medical imaging workflows, demonstrating depth in 3D rendering, MLOps, and medical imaging analysis.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
3,233
Activity Months2
Your Network
17 peopleShared Repositories
17
akashHPEMember
alemormMember
acarboniMember
Andrew MendezMember
Chandan MadhaniMember
Dale GeorgMember
caovdMember
Erdinc KayaMember
rohgeuntak76Member
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 — Focused maintenance and reliability improvements for the ai-solution-demos repository. The primary effort fixed a critical visualization issue in the Blood Vessel Analysis Demo notebook by correcting the import path for visualize_scan, switching from utils_plot to utils.plot_utils. This resolved a missing plotting utility error and enabled the full demo visualization steps to run end-to-end. The change was implemented in commit 8981e125d4a235d0327f6cc16f82ed8d1973c77a.
1 Commits
Oct 1, 2025October 2025 — Focused maintenance and reliability improvements for the ai-solution-demos repository. The primary effort fixed a critical visualization issue in the Blood Vessel Analysis Demo notebook by correcting the import path for visualize_scan, switching from utils_plot to utils.plot_utils. This resolved a missing plotting utility error and enabled the full demo visualization steps to run end-to-end. The change was implemented in commit 8981e125d4a235d0327f6cc16f82ed8d1973c77a.
October 2025
August 2025
3 Commits • 2 Features
Aug 1, 2025August 2025: Delivered two core capabilities for the ai-solution-demos repository, focusing on interactive visualization, 3D reconstruction workflows, and deployment readiness for Vista-3D integration. The Blood Vessel Geometry Analysis and 3D Reconstruction Demo App was introduced as a Streamlit-based visualization tool with a Docker build and end-to-end analysis pipeline, designed for deployment on HPE Private Cloud AI leveraging NVIDIA Vista-3D segmentation. In parallel, Vista-3D NIM Deployment Documentation and Readme Updates provide a comprehensive deployment guide for NVIDIA Vista-3D NIM models on MLIS, update the demo notebook and README to reference deployment procedures, and include a minor Kubernetes namespace adjustment to simplify production rollout. No major bugs were logged this month. These efforts enhance reproducibility, accelerate deployment, and strengthen the end-to-end vascular imaging workflow from exploration to production.
August 2025
3 Commits • 2 Features
Aug 1, 2025August 2025: Delivered two core capabilities for the ai-solution-demos repository, focusing on interactive visualization, 3D reconstruction workflows, and deployment readiness for Vista-3D integration. The Blood Vessel Geometry Analysis and 3D Reconstruction Demo App was introduced as a Streamlit-based visualization tool with a Docker build and end-to-end analysis pipeline, designed for deployment on HPE Private Cloud AI leveraging NVIDIA Vista-3D segmentation. In parallel, Vista-3D NIM Deployment Documentation and Readme Updates provide a comprehensive deployment guide for NVIDIA Vista-3D NIM models on MLIS, update the demo notebook and README to reference deployment procedures, and include a minor Kubernetes namespace adjustment to simplify production rollout. No major bugs were logged this month. These efforts enhance reproducibility, accelerate deployment, and strengthen the end-to-end vascular imaging workflow from exploration to production.
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture95.0%
Performance95.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileJupyter NotebookMarkdownPythonShellYAML
Technical Skills
3D RenderingComputer VisionData VisualizationDockerDocumentationHelmKubernetesMLOpsMedical Imaging AnalysisNVIDIA NIMPythonPython ScriptingStreamlit
Repositories Contributed To
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