aitour-leveraging-ai-for-infrastructure-management

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# BRK471 Leveraging AI for Infrastructure Management

## Session Desciption

How can IT Pros leverage AI-enhancements in Microsoft Azure? In this session, you’ll learn how Microsoft Copilot in Azure can optimize your cloud & hybrid infrastructure management, and how VS Code Copilot and other tools can take your scripting to the next level.

![image](https://github.com/user-attachments/assets/8e868ae7-8b8a-419f-a063-1a5a05a7d2b2)

## Learning Outcomes
ITPros / System Administrator will learn how they can leverage AI features and services in Azure to:
1. Get info about services in Azure that they may not be familiar with, and have Microsoft copilot in Azure guide them through deployments
2. How to manage Access to the Copilot in Azure service.
3. How to troubleshoot workloads using an AI backed process to identify issues and opportunities for improvements
4. How to leverage AI to assist in managing their infrastructure from cloud to on-prem

## Technology Used
- Azure Portal
- Microsoft Copilot in Azure
- AKS
- Azure Web Apps
- Azure Monitor
- Azure Arc
- Shell

## Additional Resources and Continued Learning

| Resources | Links | Description |
|:-------------------|:------------------------------------------------------------------------------------|:-------------------|
|Microsoft Copilot in Azure | https://azure.microsoft.com/products/copilot https://learn.microsoft.com/azure/copilot/overview https://learn.microsoft.com/azure/copilot/responsible-ai-faq https://learn.microsoft.com/azure/copilot/manage-access| What is Microsoft Copilot in Azure? What can it do? And what data does it collect? |
|Understanding deployment options | https://learn.microsoft.com/azure/copilot/deploy-vms-effectively https://learn.microsoft.com/azure/copilot/build-infrastructure-deploy-workloads https://learn.microsoft.com/azure/copilot/use-guided-deployments| Leveraging Microsoft Copilot in Azure to discover Azure services for deploying a web application.? |
|Building infrastructure and deploying workloads|https://learn.microsoft.com/azure/copilot/get-information-resource-graph https://learn.microsoft.com/azure/copilot/generate-cli-scripts https://learn.microsoft.com/azure/copilot/generate-powershell-scripts|Use Microsoft Copilot in Azure to generate AzureCLI and PowerShell scripts to deploy resources.|
|Getting Azure resource information | https://learn.microsoft.com/azure/copilot/get-information-resource-graph | Ask Microsoft Copilot in Azure to retrieve detailed information about existing resources.|
|Intro to managing existing infrastructure with AI | https://learn.microsoft.com/azure/copilot/capabilities https://learn.microsoft.com/azure/copilot/analyze-cost-management | AI can enhance IT management by automating tasks, predicting issues, and bolstering security, thus allowing IT professionals to focus on strategic initiatives and innovation. Explain the Copilot in Azure structure and capabilities, Demo the access control of that tools for portal users |
| Monitoring and metrics | https://learn.microsoft.com/azure/copilot/understand-service-health https://learn.microsoft.com/azure/copilot/query-attack-surface https://learn.microsoft.com/azure/copilot/get-monitoring-information | Let’s look at how Microsoft Copilot in Azure can help you streamlines workload monitoring by integrating AI-driven data analysis and automation for efficient management and proactive issue resolution.|
|Hybrid server management (Arc)|https://learn.microsoft.com/azure/copilot/work-smarter-edge| Learn how to extend generative AI experiences over your remote hybrid environments. You can manage your remote fleet of assets with just a few clicks.

## Content Owners

<table>
<tr>
 <td align="center"><a href="http://learnanalytics.microsoft.com">
 <img src="https://github.com/pierreroman.png" width="100px;" alt="Pierre Roman"/>
 
 Pierre Roman
 </a> 
 <a href="https://github.com/pierreroman" title="talk">📢</a> 
 </td>
 <td align="center"><a href="http://learnanalytics.microsoft.com">
 <img src="https://github.com/duffney.png" width="100px;" alt="Joshua Duffney"/>
 
 Joshua Duffney
 </a> 
 <a href="https://github.com/duffney" title="talk">📢</a> 
 </td>
</tr></table>

# Responsible AI
Microsoft is committed to helping our customers use our AI products responsibly, sharing our learnings, and building trust-based partnerships through tools like Transparency Notes and Impact Assessments. Many of these resources can be found at https://aka.ms/RAI. Microsoft’s approach to responsible AI is grounded in our AI principles of fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Large-scale natural language, image, and speech models - like the ones used in this sample - can potentially behave in ways that are unfair, unreliable, or offensive, in turn causing harms. Please consult the Azure OpenAI service Transparency note to be informed about risks and limitations.
The recommended approach to mitigating these risks is to include a safety system in your architecture that can detect and prevent harmful behavior. Azure AI Content Safety provides an independent layer of protection, able to detect harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes text and image APIs that allow you to detect material that is harmful. We also have an interactive Content Safety Studio that allows you to view, explore and try out sample code for detecting harmful content across different modalities. The following quickstart documentation guides you through making requests to the service.

Another aspect to take into account is the overall application performance. With multi-modal and multi-models applications, we consider performance to mean that the system performs as you and your users expect, including not generating harmful outputs. It's important to assess the performance of your overall application using generation quality and risk and safety metrics.

You can evaluate your AI application in your development environment using the prompt flow SDK. Given either a test dataset or a target, your generative AI application generations are quantitatively measured with built-in evaluators or custom evaluators of your choice. To get started with the prompt flow sdk to evaluate your system, you can follow the quickstart guide. Once you execute an evaluation run, you can visualize the results in Azure AI Studio.
Empowering responsible AI practices | Microsoft AI
Explore how Microsoft is committed to advancing AI in a way that is driven by ethical principles that put people first.

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