Over a two-month period, contributed to the ai-solution-eng/ai-solution-demos repository by developing agentic data interaction notebooks and a Vision Analytics Demo app. The work included building SQL/RAG routing workflows with intelligent tool selection, secure environment variable management for NVIDIA API keys, and reproducible experimentation environments using Python and Jupyter Notebooks. Enhanced deployment flexibility through Docker and Helm, enabling seamless integration with Kubernetes for scalable cloud deployment. The Vision Analytics Demo leveraged FastAPI and Gradio to analyze images, videos, and RTSP streams via a Vision Language Model, supporting structured data extraction and export, and laying groundwork for production-ready machine learning operations.
October 2025: Vision Analytics Demo app delivered for HPE Private Cloud AI; Dockerized and Helm-deployable; enables analysis of images, videos, and live RTSP streams using a Vision Language Model (VLM); supports structured data extraction and export to external systems. This marks a concrete step toward scalable, ML-powered analytics demos in production environments.
October 2025: Vision Analytics Demo app delivered for HPE Private Cloud AI; Dockerized and Helm-deployable; enables analysis of images, videos, and live RTSP streams using a Vision Language Model (VLM); supports structured data extraction and export to external systems. This marks a concrete step toward scalable, ML-powered analytics demos in production environments.
February 2025 performance summary for ai-solution-eng/ai-solution-demos focused on delivering end-to-end agentic data interaction capabilities and reproducible experimentation environments. Key features delivered include Agentic SQL/RAG routing notebooks enabling a direct tool-calling workflow, planning/verification, and an intelligent router that selects between SQL generation and RAG based on user queries. Notebook enhancements added secure handling of NVIDIA API keys via environment variables, env-based embeddings initialization, a local embedding model, graph-based conversation history, and deployment options. Environment setup and data packaging improvements established reproducible environments with a requirements.txt and packaging changes, including file renames and a compressed database file for archiving. Impact: faster prototyping of agentic data workflows, improved security and reproducibility, and a readily deployable demo suite. Technologies/skills demonstrated: LLM tooling, RAG workflows, graph-based history, embeddings (env-based and local), environment management, and deployment integration.
February 2025 performance summary for ai-solution-eng/ai-solution-demos focused on delivering end-to-end agentic data interaction capabilities and reproducible experimentation environments. Key features delivered include Agentic SQL/RAG routing notebooks enabling a direct tool-calling workflow, planning/verification, and an intelligent router that selects between SQL generation and RAG based on user queries. Notebook enhancements added secure handling of NVIDIA API keys via environment variables, env-based embeddings initialization, a local embedding model, graph-based conversation history, and deployment options. Environment setup and data packaging improvements established reproducible environments with a requirements.txt and packaging changes, including file renames and a compressed database file for archiving. Impact: faster prototyping of agentic data workflows, improved security and reproducibility, and a readily deployable demo suite. Technologies/skills demonstrated: LLM tooling, RAG workflows, graph-based history, embeddings (env-based and local), environment management, and deployment integration.
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