Combined Cloud Managed Services - wiki:ai

30day_aws_training_plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:13:35+00:00

30 Day Plan to Deploy AI in AWS Here's a 30-day (5 weeks, 5 days/week, 8 hours/day) hands-on training plan for deploying AI in AWS, focused on foundational knowledge, hands-on labs, deployment, and monitoring using key AWS AI/ML services. ----------

30day_azure_training_plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-09T20:34:42+00:00

30 Day Plan to Deploy AI in Azure Here's a 30-day learning and preparation plan to get ready to deploy and support Azure AI solutions, assuming you're working full-time (5 days/week, 8 hours/day). This plan is focused on practical skills, foundational knowledge, and hands-on experience across

30day_gcp_training_plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-09T20:39:12+00:00

30 Day Plan to Deploy AI in GCP Here’s a 30-day training plan to get ready for deploying and supporting AI on Google Cloud Platform (GCP), assuming you’re working full-time (5 days/week, 8 hours/day). This plan is focused on practical experience with

advanced_rag_implementation_and_example_code

Anonymous (anonymous@undisclosed.example.com) — 2025-09-03T16:34:30+00:00

Advanced RAG Implementation in Azure Prerequisites * Azure Web App Resource Created * Azure Bot Resource Created * Key Vault Created * OpenAI Chat Bot created * OpenAI Embedding Model Created * Azure AI Search Resource and Index created

affirm_onboarding

Anonymous (anonymous@undisclosed.example.com) — 2026-04-11T04:15:47+00:00

Affirm Onboarding The following document is an overview of the onboarding process with Affirm. It will cover the following: what is our responsibility, what is Synergist's responsibility, and the steps that should be taken. (Note: This is a first draft and will be revised after the completion of CloudGenie onboarding)

affirm_snow_process

Anonymous (anonymous@undisclosed.example.com) — 2026-01-14T18:24:39+00:00

Adding Affirm environment to SNOW The process for adding an Affirm environment to our Service-NOW is very simple. On our side we need to submit a ticket to create an API account for Affirm to connect to, and that's it. The rest is up to the Affirm team. The following is an example ticket that was used to connect their Staging environment to our Prod. SNOW:

affrim_knowledge

Anonymous (anonymous@undisclosed.example.com) — 2026-01-14T18:14:18+00:00

Affirm Documentation Process documents and team notes regarding Synergists tool 'Affirm' ---------- Process Documents and How-to's * Adding Affirm environment to SNOW * Onboarding Affirm to Clients Model ---------- Misc. Notes * Suggestions and Criticism for Affirm (Old) AI Homepage

agentic_rag

Anonymous (anonymous@undisclosed.example.com) — 2025-09-12T20:01:52+00:00

Agentic RAG This document provides an overview and explanation of the code used to create a Semantic Kernel-based tool that integrates with Azure AI Search for Retrieval-Augmented Generation (RAG). In this document we have an AI agent that retrieves inflation rates from an Azure AI Search index, augments user queries with semantic search results, and streams detailed answers about the consumer price index.

ai_agent_production_architecture_with_rag_and_mcp

Anonymous (anonymous@undisclosed.example.com) — 2025-11-07T16:16:26+00:00

Phase 1 – Application Setup and Deployment This phase established the foundation for the AI Platform by deploying three independent web applications into Azure App Service using GitHub Actions. Each app represents a distinct layer of the AI orchestration pipeline (Agent Gateway → MCP Server → RAG

ai_beginner_s_training_resources_v1.0

Anonymous (anonymous@undisclosed.example.com) — 2025-05-29T14:21:35+00:00

AI Beginner’s Training This training path introduces you to the fundamentals of cloud computing with Azure and AI services. It includes foundational Microsoft Learn courses and resources from other 3rd party learning sites. These training resources will be a good starting point to help you familiarize yourself with Azure resources and some of the fundamentals of working with AI in Azure.

ai_knowledge

Anonymous (anonymous@undisclosed.example.com) — 2026-02-12T17:41:27+00:00

AI Knowledge 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 * AI Fundamentals * Azure OpenAI Documentation * Key Terms for AI ---------- LLM * LLM Fundamentals * comparison of chatgpt models Best Practices

ai_sow_analyzer

Anonymous (anonymous@undisclosed.example.com) — 2026-01-15T22:52:29+00:00

SOW Analyzer – Teams Bot Application 1. Purpose and Scope This document provides full technical documentation for the SOW Analyzer Teams Bot application. It is intended for: * Engineers maintaining or extending the system * Security and architecture reviewers

ai-operational-plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-28T17:37:49+00:00

Operational Plan: Managed AI Services Team 1. Team Structure & Roles Role Responsibilities AI Platform Engineer Deploy, monitor, and manage LLMs on cloud and on-prem infrastructure DevOps Engineer Automation scripts, CI/CD for model updates, shell-level ops, infrastructure

automl_lab

Anonymous (anonymous@undisclosed.example.com) — 2025-06-18T13:39:49+00:00

Project Summary Model Name Loan Default Predictor Purpose Predict whether a loan applicant is likely to default on a loan based on personal, financial, and credit attributes. Model Type Classification Algorithm Source AutoML (Azure Machine Learning)

azure_chatbot_with_open_ai_and_slack_integration_2

Anonymous (anonymous@undisclosed.example.com) — 2025-07-17T19:53:17+00:00

Azure Chatbot with OpenAI and Slack Integration 2 This document provides instructions on how to deploy an application that uses OpenAI for a chatbot and Azure Bot to integrate into a Slack channel. This Wiki details an alternative way to integrate an Azure chatbot with Slack.

azure_chatbot_with_open_ai_and_slack_integration

Anonymous (anonymous@undisclosed.example.com) — 2025-06-27T21:17:18+00:00

Azure chatbot with open AI and slack integration Complete all the steps in the documentation Azure chatbot with Open AI ( These steps are run in local ) npm install botbuilder-adapter-slack npm install @slack/web-api index.js const path = require('path'); const restify = require('restify'); const { BotFrameworkAdapter } = require('botbuilder'); const { AzureOpenAIBot } = require('./bot'); // Load environment variables const ENV_FILE = path.join(__dirname, '.env'); require('dotenv').config(…

azure_chatbot_with_open_ai_integration

Anonymous (anonymous@undisclosed.example.com) — 2025-07-10T21:40:15+00:00

Azure chatbot with Open AI integration In this lab we will be building an azure chat bot which is integrated with Open AI to answer the user's questions. This bot can be integrated to slack,teams etc. In this lab we will integrate it with slack for testing

azure_function_app_to_perform_image_blur

Anonymous (anonymous@undisclosed.example.com) — 2025-07-03T18:20:34+00:00

Deploy Face Blur Script to Azure Function App Prerequisites Install Azure CLI # Windows curl -sL | sudo bash # macOS brew install azure-cli # Or download from: Install Azure Functions Core Tools # Windows (via npm) npm install -g azure-functions-core-tools@4 --unsafe-perm true

azure_how-to

Anonymous (anonymous@undisclosed.example.com) — 2025-08-13T20:18:07+00:00

Azure * How to login to a our sandbox via CLI * Azure OpenAi Chatbot Deployment Checklist * How to View Spending and Budgets * Microsoft Purview Features and Setup AI How-to

azure_login

Anonymous (anonymous@undisclosed.example.com) — 2025-05-13T13:24:12+00:00

To login to a our sandbox via CLI: az login --tenant siriusazuretest.onmicrosoft.com This will bring up a browser window to login and then you will select AI Team from the list: az login --tenant siriusazuretest.onmicrosoft.com A web browser has been opened at https://login.microsoftonline.com/6ad27142-5e7d-4841-9e72-c3aaca00f9e6/oauth2/v2.0/authorize. Please continue the login in the web browser. If no web browser is available or if the web browser fails to open, use device code flow wit…

azure-ai-services-getting-started

Anonymous (anonymous@undisclosed.example.com) — 2025-05-27T18:33:21+00:00

---------- Getting Started with Azure AI Services This document will walk you through where to go in Azure AI Foundry to get started with some of Microsoft's out-of-the-box AI services. Step 1: Deploy an Azure AI Foundry resource and navigate to

azure-api-monitoring

Anonymous (anonymous@undisclosed.example.com) — 2025-05-28T16:43:24+00:00

Azure API Monitoring Monitoring Azure AI APIs is critical for performance, usage tracking, quota management, and troubleshooting. Azure provides multiple built-in and extensible options to monitor its AI services (like Azure OpenAI, Cognitive Services, and Azure Machine Learning). Here's a breakdown of the available monitoring options:

base_command_manager_overview

Anonymous (anonymous@undisclosed.example.com) — 2026-03-27T22:01:56+00:00

Base Command Manager Overview * NVIDIA Academy - Base Command Manager Administration Course * NVIDIA Base Command Manager Documentation NVIDIA Training Resources

bicep-terraform-comparison

Anonymous (anonymous@undisclosed.example.com) — 2025-06-11T14:31:34+00:00

Bicep Terraform Comparison Conclusion I worked on two scripts, both doing the same thing, one in Bicep and one in Terraform. They both produced the exact same results. The biggest difference in the process is that bicep is more integrated with the Azure command line and needs fewer commands to get the same thing done. Terraform is easier to read through as Json is not the greatest for code review, although it's not difficult. Terraform is also better suited for multi-cloud support.

chatbot-helpdesk-sentiment

Anonymous (anonymous@undisclosed.example.com) — 2025-07-02T21:10:02+00:00

AI Chatbot with Sentiment and In Kind Responses Requirements ✅ 1. Azure Resources Checklist Resource Type Name (per your setup) Notes Function App dehamerfuncapp Your C# Azure Function app Storage Account Linked to Function App Required for deployment—should auto-create if

clearinghouse_sow_analyzer

Anonymous (anonymous@undisclosed.example.com) — 2026-02-12T17:41:59+00:00

SOW Analyzer – Technical User Guide & Reference Manual 1. System Overview The SOW Analyzer is a Microsoft Teams-integrated AI validation system that evaluates uploaded Statements of Work (SOWs) against predefined “golden” template documents.

cli-workspace-deployment-upload

Anonymous (anonymous@undisclosed.example.com) — 2025-06-04T15:51:36+00:00

CLI ML Workspace Transcript This turned out to take way longer than expected due to ChatGPT getting into a loop and breaking one thing to fix another and then not keeping track of it's own changes. To finally fix this I had to point out a flaw in it's process so it gave me the right script and then it finally worked as shown below. Beneath the scripts is the transcript of the conversations to get me to the working end product.

cloud_ai_training_plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-14T12:15:57+00:00

Cloud AI Training Plan Phase Audience Topics Courses Duration Phase 1: Core AI/ML Specialization All team members ML Fundamentals, ML in IT Ops, Responsible AI Google ML Crash Course, AI for Everyone, AWS ML Essentials Weeks 1–4 Phase 2: MLOps, Automation & Monitoring

comparison_of_chatgpt_models

Anonymous (anonymous@undisclosed.example.com) — 2025-06-02T13:35:32+00:00

Feature GPT-3.5 GPT-4.0 GPT-4.5 (Turbo) ------------------------------------------------------------------------------------- Access Free-tier API only (limited) ChatGPT Plus &

computer_vision_and_translate_lab

Anonymous (anonymous@undisclosed.example.com) — 2025-05-30T19:35:29+00:00

Image Text Detection to Translation Demo Resources to Create Note: For the resources that are being used, you will need to allow access to your IP via the Networking tab for each resource. Prerequisites * Key Vault created to store the API keys of both the Computer Vision and Language AI Resources.

deploy-azure-key-vault

Anonymous (anonymous@undisclosed.example.com) — 2025-05-20T14:32:26+00:00

Deploy Azure Key Vault Creating an Azure Key Vault - Don's Version * Login to the correct subscription. * In the Search Bar search for Key Vault and select Key Vaults * Click Create in upper left * Select the correct subscription and resource group, which has to already exist.

dgx-spark-monitoring

Anonymous (anonymous@undisclosed.example.com) — 2026-04-17T11:36:15+00:00

DGX Spark Monitoring Stack Overview * nv-monitor: single C binary that monitors CPU, GPU, memory and exposes Prometheus metrics * Prometheus: scrapes and stores metrics from nv-monitor every 5 seconds * Grafana: visualizes metrics in a live dashboard

docker_cheatsheet

Anonymous (anonymous@undisclosed.example.com) — 2026-01-21T22:00:49+00:00

Docker Basics – Command Reference Docker Installation and Health Checks docker version Purpose Verifies that the Docker CLI is installed and whether it can communicate with the Docker daemon. What it shows Client version information and server (daemon) version information.

emotional_support_bot

Anonymous (anonymous@undisclosed.example.com) — 2025-07-21T04:37:27+00:00

Emotional support Bot with Text Analytics and OpenAI This app is deployed in static webapp and uses node JS for managed Azure functions. Azure functions version 4 is used. Below is the folder structure that needs to be deployed for Azure functions V4

export_to_pdf

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:36:48+00:00

Export to PDF Just use the Export to PDF button on the right (looks like and adobe symbol) Usage There are three ways to export: * Export one page * You simply need to call the page with an additional do=export_pdf URL parameter. This starts the PDF export of the page. Some templates let the plugin extend the pagetools automatically. For other templates you need to implement this yourself to add a button or similar somewhere in your template design.

foundational_goals

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:35:47+00:00

Mission Statement for Documentation Running a cloud managed services team requires having several policies in place to ensure that the team operates efficiently and effectively. Here are some policies that you should consider implementing: * Service Level Agreements (SLAs): Establish SLAs that define the expected response and resolution times for incidents and service requests. This will help you manage customer expectations and provide consistent service.

github_ml_pipeline

Anonymous (anonymous@undisclosed.example.com) — 2025-08-08T16:12:31+00:00

Github ML Pipeline Github repository structure mlops-pipeline.yml name: MLOps CI/CD Pipeline on: push: branches: [ main ] pull_request: branches: [ main ] env: AZURE_ML_WORKSPACE: ${{ secrets.AZURE_ML_WORKSPACE }} AZURE_RESOURCE_GROUP: ${{ secrets.AZURE_RESOURCE_GROUP }} AZURE_SUBSCRIPTION_ID: ${{ secrets.AZURE_SUBSCRIPTION_ID }} MODEL_NAME: "customer-churn-model" ENDPOINT_NAME: "churn-prediction-endpoint" permissions: id-token: write contents: read jobs: …

github-actions-azure-pipeline

Anonymous (anonymous@undisclosed.example.com) — 2025-06-20T14:58:24+00:00

Azure Github Actions Pipeline Azure ML Deployment Debug Summary (Detailed) 1. Summary of Steps Taken This session involved identifying and resolving deployment issues for an Azure ML online endpoint named `don-iris-endpoint`. The deployment initially worked but began failing after changes were made to the `score.py` script to add monitoring features.

home-page

Anonymous (anonymous@undisclosed.example.com) — 2026-03-26T22:31:33+00:00

AI Cloud Managed Services Policies and Procedures * Foundational Goals * Export to PDF * Cheat Sheet for formatting * Useful Syntax * Wiki Formatting Examples ---------- Information * MS AI Team Jira Board * CDW ChatGPT * CDW CoPilot * Azure Portal * Top 10 LLM Applications in the Cloud * 30 Day Plan to Deploy AI in Azure * 30 Day Plan to Deploy AI in GCP * 30 Day Plan to Deploy AI in AWS * Role Based AI Training Plan * Cloud AI Training Plan * Training Resour…

how-to

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T13:44:32+00:00

How-To * Azure * NVIDIA AI Home

key_terms_ai

Anonymous (anonymous@undisclosed.example.com) — 2025-05-28T16:55:26+00:00

Key Terms for AI Term / Technology Category Definition / Description Artificial Intelligence (AI) Concept Simulation of human intelligence by machines, especially computer systems. Machine Learning (ML) Concept Algorithms that allow computers to learn patterns and make decisions with data.

key-vault-best-practices

Anonymous (anonymous@undisclosed.example.com) — 2025-05-20T13:39:53+00:00

Azure Key Vault Best Practices 📘 Overview Azure Key Vault is a cloud service for securely storing and accessing secrets, keys, and certificates. To maximize security, it's critical to follow industry-standard and Microsoft-recommended practices.

kubectl_cheatsheet

Anonymous (anonymous@undisclosed.example.com) — 2026-02-27T19:57:39+00:00

kubectl Cheat Sheet CLUSTER LIFECYCLE COMMANDS * minikube start * Starts a local Kubernetes cluster * Automatically configures kubectl to point to the cluster * minikube stop * Stops the cluster without deleting it * minikube delete

kubernetes_cheatsheet

Anonymous (anonymous@undisclosed.example.com) — 2026-01-27T19:53:35+00:00

Kubernetes Cheat Sheet Kubernetes Architecture Overview Control Plane (management layer) * kube-apiserver: central access point for all operations * etcd: persistent state store * kube-scheduler: assigns Pods to nodes * controller-manager:

lie-detector_application

Anonymous (anonymous@undisclosed.example.com) — 2025-06-18T13:29:19+00:00

Lie Detector AI Web Application – Documentation Overview The Lie Detector AI is a Flask-based web application that: * Accepts an audio file from the user * Transcribes the speech using Azure Speech Services * Analyzes sentiment using Azure Language Service

llm_fundementals

Anonymous (anonymous@undisclosed.example.com) — 2025-05-15T13:28:54+00:00

LLM Fundamentals This page goes over and links different fundamental topics describing Large Language Models (LLMs) and how they work, what their role is within AI software, and how to learn more about them ---------- Models There are several different models that LLM use to predict and provide different results. Click on the links to learn more about the models that fall into each category.

llm_regression

Anonymous (anonymous@undisclosed.example.com) — 2025-05-15T13:30:46+00:00

Regression Regression models are trained to predict numeric label values based on training data that includes both features and known labels. The process for training a regression model (or indeed, any supervised machine learning model) involves multiple iterations in which you use an appropriate algorithm (usually with some parameterized settings) to train a model, evaluate the model's predictive performance, and refine the model by repeating the training process with different algorithms and …

managed-ai-service-offering

Anonymous (anonymous@undisclosed.example.com) — 2025-05-14T12:07:34+00:00

CDW AI Consult and Assess AI Strategy and Implementation involve helping businesses craft their AI strategy and develop industry and process-specific solutions. This includes evaluating the current state of AI adoption and identifying gaps and opportunities, creating a strategic plan for AI integration with short-term and long-term goals, providing training and development programs to upskill employees in AI technologies, and navigating the legal and ethical aspects of AI implementation to ensu…

ml_lab_-1_create_azure_ml_workspace_and_explore_studio_interface

Anonymous (anonymous@undisclosed.example.com) — 2025-06-05T21:20:43+00:00

Hands-on Lab 1: Create Azure ML Workspace and Explore Studio Interface ## Learning Objectives By the end of this lab, you will: - Create an Azure Machine Learning workspace - Navigate the Azure ML Studio interface - Understand key components and navigation structure - Explore workspace assets and settings - Configure basic workspace settings

ml_lab_-2_set_up_compute_resources_and_create_your_first_dataset

Anonymous (anonymous@undisclosed.example.com) — 2025-06-05T21:34:16+00:00

# Hands-on Lab 2: Set up Compute Resources and Create Your First Dataset # ## Learning Objectives By the end of this lab, you will: - Create and manage compute instances for development - Set up compute clusters for training workloads - Upload data to Azure ML datastores - Create and register datasets - Explore data using Azure ML tools - Understand compute cost management

ml_lab_-3

Anonymous (anonymous@undisclosed.example.com) — 2025-06-11T13:21:29+00:00

Hands-on Lab 3: Build a Complete ML Pipeline Using AutoML and Designer 🧭 Objective In this lab, you will: Use Azure Machine Learning Designer for a visual ML pipeline Build and evaluate a model using AutoML Compare both methods Deploy the best model

ml_lab_-4_train_a_model_using_python_sdk_v2_in_notebook

Anonymous (anonymous@undisclosed.example.com) — 2025-06-11T22:28:50+00:00

🎯 Objective Train a machine learning classification model (Titanic survival prediction) using Python SDK v2 in Azure ML. You'll go through loading data, setting up the workspace, tracking experiments, and submitting a training job. 📦 Prerequisites Before starting this lab, you should have:

ml-labs

Anonymous (anonymous@undisclosed.example.com) — 2025-06-06T18:25:09+00:00

ML lab lessons learnt Ml job timing out The ML job may timeout after 6 hours and will have a warning saying it timed out in that case check the limits of the job configured I had set the below configuration So the max trials is set to 1000 which makes the job to use 1000 different models to evaluvate. So always ensure you have right number of trials selected.

ml-pipeline-test

Anonymous (anonymous@undisclosed.example.com) — 2025-06-06T13:46:18+00:00

Azure ML Pipeline Test Purpose Test deployment of Azure ML Pipeline and look at outputs and download trained model. Key Things Learned * When you create train.py and prep.py or any other environment files, they should be stored under ./src in your notebook directory.

ml-workspaces

Anonymous (anonymous@undisclosed.example.com) — 2025-06-03T14:42:55+00:00

ML Workspaces * When deploying workspace, don't pick no public access unless you have a private DNS domain configured and available or you won't be able to deploy any compute resources and get weird errors that make zero sense. * Notebooks are an Azure IDE that can tie into VS Code, either Web or Desktop, or launch a terminal to test code on a deployed compute instance.

new_co-worker_onboarding

Anonymous (anonymous@undisclosed.example.com) — 2026-04-08T15:52:52+00:00

Purpose This onboarding document outlines the essential information and processes required for a new engineer to start working with the Hybrid AI Managed Services Team. Scope This document outlines the personal onboarding process for new engineers including the below

notebook-collectibles-deployment

Anonymous (anonymous@undisclosed.example.com) — 2025-06-05T12:23:15+00:00

Azure ML V2: Deploy and Query ML Model from CSV Process I exported my home MySQL database of collectibles to a csv file using: mysql -u root -p -e "SELECT * FROM collectibles.inventory;" | sed 's/\t/","/g;s/^/"/;s/$/"/' > collectibles.csv That gave me a file that looked like this:

nvidia_bcm-bringing-up-cluster

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T16:25:44+00:00

Back to NVIDIA page Back to BCM page Topic: Bringing Up the Cluster (Post-Installation Tasks) What This Unit Covers: The complete post-installation workflow required to move a newly installed NVIDIA Base Command Manager cluster from a basic head-node installation into a usable, provisioned cluster. What must be done on the head node before compute nodes can be safely powered on and provisioned. Licensing, software updates, software image preparation, category design, node assignment, identif…

nvidia_bcm-installation

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T17:18:29+00:00

Back to NVIDIA page Back to BCM page NVIDIA Base Command Manager Installation Manual Topic: Installation Overview and Graphical Installer What This Unit Covers * The main BCM installation approaches * Why the graphical installer is the preferred path for most standard deployments * What information should be prepared before installation begins

nvidia_bcm-intro

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T16:49:25+00:00

Back to NVIDIA page Back to BCM page Base Command Manager (BCM) Intro What NVIDIA Base Command Manager Is NVIDIA Base Command Manager (BCM) is NVIDIA’s cluster management platform for AI and HPC environments. Its purpose is to streamline cluster provisioning, workload management, and infrastructure monitoring

nvidia_bcm-managing-nodes

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T18:28:53+00:00

Back to NVIDIA page Back to BCM page Topic: Managing Nodes & Software Images What This Unit Covers Creating, adding, and cloning nodes * Understanding node categories vs node groups * Creating, cloning, updating, and assigning software images * Synchronizing changes in both directions:

nvidia_bcm-networking

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T16:35:17+00:00

Back to NVIDIA page Back to BCM page Topic: Networking and Preparing for Installation What This Unit Covers: This unit is really about the planning work that has to happen before you install BCM on the head node. NVIDIA’s installation and deployment documentation treats networking, licensing, ISO preparation, and site-survey data as foundational prerequisites rather than afterthoughts. In practice, that means you should know your topology, IP plan,

nvidia_bcm

Anonymous (anonymous@undisclosed.example.com) — 2026-04-07T14:28:28+00:00

* Intro * Networking and Preparing for Installation * Installation Overview * Bringing up the Cluster * Managing Nodes and Software Images * Monitoring the Cluster * User and Group Management * Workload Management * Cloud Integration * Autoscaler

nvidia_how-to

Anonymous (anonymous@undisclosed.example.com) — 2026-04-17T09:56:36+00:00

* BCM - Base Command Manager * DGX Spark Monitoring

nvidia_training-resources

Anonymous (anonymous@undisclosed.example.com) — 2026-03-26T22:03:11+00:00

NVIDIA Training Resources * NVIDIA Managed Services Training Spreadsheet * Base Command Manager Overview AI Cloud Managed Services Policies and Procedures

old_affirm_notes

Anonymous (anonymous@undisclosed.example.com) — 2026-01-14T18:13:09+00:00

Suggestions * Give explanation for why each monitoring test failed – 'Toxicity test failed due to these words:' * Functionality for running multiple initial monitoring tests at once or creating a queue. * Ability to evaluate models against the framework packs, rather than a simple checklist

openai-chatbot-deployment-checklist

Anonymous (anonymous@undisclosed.example.com) — 2025-05-16T19:14:23+00:00

Azure Open AI Chatbot Deployment Checklist ✅ Azure OpenAI Chatbot Deployment Checklist 1. Local Build and Testing * Bot code tested locally with Bot Framework Emulator. * Environment variables for AZURE_OPENAI_ENDPOINT, DEPLOYMENT_ID, and API

operational_processes

Anonymous (anonymous@undisclosed.example.com) — 2025-12-17T03:57:48+00:00

Operational Processes ---------- Team Standards * Team Standards v1.0 * Managing Priorities * Training Wiki Access * Wiki Public IP Change Process * Wiki Password Reset

point-paper-ai

Anonymous (anonymous@undisclosed.example.com) — 2025-05-14T12:06:53+00:00

Point Paper: AI Governance-as-a-Service (AI-GaaS) Positioning the Multi-Cloud Practice Goal: CDW becomes the dedicated AI Governance-as-a-Service (AI-GaaS) provider for Synergist clients, delivering consistent onboarding, operations, and compliance management across AWS, Azure, and Google Cloud.

potential-monitoring-plan

Anonymous (anonymous@undisclosed.example.com) — 2025-06-18T13:34:05+00:00

Potential Monitoring Plan Overview This Azure AI Monitoring and Reporting Plan provides enterprise-grade visibility, accountability, and operational assurance for AI workloads running in Microsoft Azure. Designed for applications leveraging services like Azure OpenAI, Speech, Language, and Cognitive APIs, this plan ensures your AI solutions remain secure, reliable, and performance-optimized.

presales-info

Anonymous (anonymous@undisclosed.example.com) — 2025-05-14T11:59:47+00:00

Presales Resources * Point Paper on AI * Managed AI Service Offering AI Cloud Managed Services Policies and Procedures

purview-features

Anonymous (anonymous@undisclosed.example.com) — 2025-08-13T20:25:52+00:00

---------- Microsoft Purview Features and Setup The information and instructions in this document apply to the Microsoft Purview Governance Portal. To access this portal, go to the purview account in the Azure portal, and select “Open Microsoft Purview Governance Portal (new)

rag-app

Anonymous (anonymous@undisclosed.example.com) — 2025-08-06T14:06:46+00:00

RAG Application Documentation Overview This Retrieval-Augmented Generation (RAG) application integrates Azure OpenAI, Azure AI Search (formerly Azure Cognitive Search), and Azure Blob Storage to enable document ingestion, vector embedding, indexing, and querying of content using natural language.

responsible-ai-test

Anonymous (anonymous@undisclosed.example.com) — 2025-06-16T20:09:51+00:00

Responsible AI Test Purpose Evaluate the Responsible AI dashboard and see what it does. Test Process Here's a structured list of Responsible AI Dashboard Deployment Steps using the corrected scripts. Each step includes: * ✅ Step Number & Action

role_based_ai_training_plan

Anonymous (anonymous@undisclosed.example.com) — 2025-05-14T12:14:49+00:00

Role Based Cloud AI Training Plan Role Topics Courses/Certifications Recommended Duration ML Engineers ML Fundamentals, Time Series, Model Deployment Google ML Crash Course, Fast.ai, AWS ML Specialty, Coursera MLOps Weeks 1–12 DevOps / MLOps Engineers

sample-python-chatbot

Anonymous (anonymous@undisclosed.example.com) — 2025-05-28T14:31:33+00:00

Sample Python Chatbot This requires python with the os, tkinter (tk/ttk), re, time, and openai modules to work. #!/usr/bin/python # replace with path to installed python import os import tkinter as tk from tkinter import ttk, messagebox import re import time from openai import AzureOpenAI, OpenAIError # === Azure OpenAI Configuration === endpoint = "https://.openai.azure.com/" deployment = "" subscription_key = "" api_version = "" # such as 2…

sample-workbook-monitor-openai

Anonymous (anonymous@undisclosed.example.com) — 2025-05-28T16:45:47+00:00

Sample Workbook to Monitor Azure OpenAI Azure Workbook: Monitor Azure OpenAI How to Set It Up * Go to the Azure Portal. * Navigate to Monitor > Workbooks. * Click + New to create a new workbook. * Click the “ Advanced Editor” tab. *

speech-translate_lab

Anonymous (anonymous@undisclosed.example.com) — 2025-05-21T20:44:23+00:00

Azure Speech & Translator with Key Vault Integration 🥤 Overview This setup securely connects to Azure Speech-to-Text and Translator services using secrets stored in Azure Key Vault, accessed via `DefaultAzureCredential`. --- 🔐 Azure Key Vault Setup

spending-and-budgets

Anonymous (anonymous@undisclosed.example.com) — 2025-05-30T15:00:43+00:00

---------- How to View Spending and Budgets This document will walk through how to view spending in Azure, as well as how to view and set budgets. Step 1: In the Azure Portal, go to Cost Management. In the left pane, select Cost analysis Step 2:

study_materials_for_nvidia_certified_associate_-_ai_infrastructure_operations

Anonymous (anonymous@undisclosed.example.com) — 2026-02-17T23:03:49+00:00

Module 1: Fundamentals * Drivers of Evolution * Increase in amount of Data * Computational power growth * Algorithm breakthroughs * AI use cases across industries * Automotive * Real time object detection * Autonomous decision making

supervised_llm

Anonymous (anonymous@undisclosed.example.com) — 2025-05-15T13:30:01+00:00

Supervised LLM This page describes the different LLMs that fall under the Supervised category. ---------- * Regression * Binary Classification * Multiclass Classification LLM Fundamentals

team_standards_v1.0

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:43:40+00:00

AI Managed Services Team Standards Administrative * Sprints 1 week * Start on Monday end following Monday prior to Sprint Planning * Demo every Friday as part of the Sprint Review * Daily Scrum at 9:00 am Central Time attendance is mandatory.

testing-gpt-models

Anonymous (anonymous@undisclosed.example.com) — 2025-06-18T13:55:17+00:00

MAIT-510 – Learn Azure OpenAI: GPT Model Overview and Comparison Model Tokens/Minute (TPM) Requests/Minute (RPM) Latency Throughput Error Handling gpt-4o Up to 450K per region

top10llm-applications

Anonymous (anonymous@undisclosed.example.com) — 2025-05-09T20:31:33+00:00

Top 10 LLM Applications in the Cloud Here are the top 10 LLM (Large Language Model) applications that can be effectively built using cloud AI platforms like AWS, Azure, Google Cloud, or OpenAI APIs: ---------- 1. Intelligent Chatbots & Virtual Assistants

training-resources

Anonymous (anonymous@undisclosed.example.com) — 2026-02-17T23:02:14+00:00

Training Resources * AI Beginner's Training Resources v1.0 * AI-900 AI Fundamentals * Azure AI Services * Azure AI Studio * Azure OpenAI * Azure ML Notebooks * AI-102 Study Guide * Azure OpenAI Service Training - Prompt engineering and API integration using GPT models * Secure and manage secrets with Azure Key Vault - Essential for protecting AI and ML credentials * Azure AI samples on GitHub - Microsoft’s official repository for AI and ML quickstarts and solutions * Micro…

video-keyword-transcription

Anonymous (anonymous@undisclosed.example.com) — 2025-06-02T12:41:02+00:00

Video Keyword Transcription 🧾 Azure Video + Text Analytics Demo Summary 🧩 Service Deployment * Azure Video Indexer (Trial or Full Account) * Created or accessed via videoindexer.ai or the Azure Portal. * Required: Account ID and API Key (from

webapp-chatbot-deployment

Anonymous (anonymous@undisclosed.example.com) — 2025-06-13T18:01:57+00:00

Azure Webapp Deployment 📁 Final File Contents (cloudadvisor.zip) The archive used for deployment contained: * app.py – Flask app connecting to Azure OpenAI, handling routing and logic * requirements.txt – Dependencies: flask and openai

wiki_password_reset

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:41:16+00:00

Wiki Password Reset * Go to this link: Wiki Password Reset * Domain\user name example: Siriuscloud\firstinititallastname * Example: Sirius\slee Operational Processes

wiki_public_ip_change_process

Anonymous (anonymous@undisclosed.example.com) — 2025-05-12T12:40:46+00:00

Wiki Public IP Change Process IP specific configurations has been removed from the Dokuwiki to avoid issues with the frequent changes on the Indian team side. The site is protected by AD and MFA. Operational Processes

x-posts-sentiment-analysis

Anonymous (anonymous@undisclosed.example.com) — 2025-07-17T18:59:48+00:00

Product Sentiment Analysis App This is a web application that searches X/Twitter for tweets about a product, analyzes their sentiment using Azure Cognitive Services and Azure OpenAI, and displays a summary. Infrastructure Required Services * Azure OpenAI Service