This is where the MS-AI-Team documents and stores different notes and findings about different AIs and LLMs; how they work, their limitations, and methods for improvement.

• AI Fundamentals
• Azure OpenAI Documentation
• Key Terms for AI

• LLM Fundamentals
• comparison of chatgpt models

• Azure Key Vault Best Practices
• Deploy Azure Key Vault
• Azure API Monitoring
• Sample Workbook to Monitor OpenAI

• Getting Started with Azure AI Services
• Advanced RAG Implementation and Example Code
• Speech and translate
• Computer Vision
• Sample Python Chatbot
• Video Keyword Transcription
• Computer Vision and Translate
• ML lab -1 Create Azure ML Workspace and Explore Studio Interface
• ML lab -2 Set up Compute Resources and Create Your First Dataset
• ML lab 3
• ML lab 4 Train a Model Using Python SDK v2 in notebook
• Lie-Detector Application
• AutoML Lab using Loan Data
• Azure chatbot with Open AI integration
• Azure chatbot with Open AI and slack integration
• Azure chatbot with Open AI and slack integration 2
• Azure Function app to perform Image blur
• Twitter/X Product Sentiment Analysis
• Azure Text analytics & Azure OpenAI
• Retrieval-Augmented Generation (RAG) Application
• Github ML pipeline
• Agentic RAG
• AI Agent Production Architecture with RAG and MCP
• AI SOW Analyzer
• NVIDIA GPU Operator Installation and Upgrade

• AI Operational Plan
• Potential Monitoring Plan/Capabilities

• ML Work Spaces
• CLI Workspace Deployment Transcript
• Notebooks Collectibles Deployment
• Responsible AI Test
• Azure ML Pipeline Test
• ml-labs
• Bicep Terraform Comparison
• Azure Webapp Deployment
• Azure GitHub Actions Pipeline
• Testing GPT-4.1 vs GPT-4o
• AI Helpdesk with Sentiment and Responses

* NVIDIA Network Operator on Kubernetes — Deployment, Configuration & Lifecycle Reference

* Clearinghouse SOW Analyzer

AI Team Homepage