Instructor-led 4-days

Course Description:

This 4-Part intensive hands-on course provides a comprehensive exploration of Microsoft Azure cloud infrastructure through the integration of AI-driven tools and techniques. Participants will learn how to use AI to automate configuration, monitor and manage resources, enhance security, and troubleshoot Azure environments. Through hands-on labs, case studies, and guided exercises, attendees will gain practical experience with Azure-specific AI services, such as Azure Machine Learning, Azure Monitor, and Azure Security Center, to streamline cloud operations, improve resource allocation, and proactively address potential issues. By the end of the course, participants will be equipped to harness AI for building, managing, and troubleshooting scalable and resilient Azure cloud environments efficiently and securely.

Learning Objectives:

1. Understand the fundamentals of Azure cloud infrastructure and how AI tools can improve its management and optimization.
2. Configure Azure environments using AI-driven tools to automate setup and optimize performance.
3. Build resilient and scalable cloud architectures on Azure with AI to enhance resource management and cost efficiency.
4. Manage and monitor Azure environments with AI, identifying and mitigating issues proactively.
5. Troubleshoot Azure cloud challenges using AI-powered predictive analysis and machine learning techniques.
6. Apply security best practices for AI in Azure deployments to maintain compliance and mitigate vulnerabilities.
7. Demonstrate proficiency in implementing AI solutions to support high-performance, cost-effective, and resilient Azure cloud infrastructures.

Part 1: Introduction to Azure Cloud and AI Integration

Module 1: Foundations of Microsoft Azure Cloud Infrastructure

• Topics Covered:
  • Overview of Azure services and cloud models (IaaS, PaaS, SaaS)
  • Key components of Azure: Virtual Machines, Storage, Networking, Databases
  • Familiarization with Azure Portal, CLI, and Resource Manager (ARM)
• Hands-on Labs:
  • Setting up an Azure account and navigating the Azure Portal
  • Creating and managing basic Azure resources (VMs, storage accounts, and networking)

Module 2: AI in Azure Cloud Infrastructure

• Topics Covered:
  • Introduction to AI in cloud infrastructure: benefits, use cases, and tools
  • Overview of AI-powered Azure tools for cloud management (Azure Machine Learning, Cognitive Services, Azure Synapse)
  • Ethical considerations and compliance for AI in cloud environments
• Hands-on Labs:
  • Exploring AI features within Azure (Azure Machine Learning, Synapse Analytics)
  • Identifying use cases for AI-driven management on Azure

Part 2: Configuring and Building Azure Cloud Infrastructure Using AI

Module 3: AI-Driven Azure Configuration

• Topics Covered:
  • Using Azure Automation, Machine Learning, and Logic Apps for automated configuration management
  • Azure Resource Manager (ARM) templates and best practices for configuration as code
  • Using AI models for analyzing and recommending configuration setups
• Hands-on Labs:
  • Creating and deploying ARM templates with AI-enhanced optimization suggestions
  • Using AI in Logic Apps and Machine Learning for automated configuration tasks in Azure

Module 4: Building Scalable and Optimized Architectures in Azure

• Topics Covered:
  • Designing scalable Azure architectures with AI to optimize performance and costs
  • Implementing auto-scaling with Azure Monitor, AI-driven scaling, and Azure Load Balancer
  • Using Azure Machine Learning to predict workload patterns and allocate resources efficiently
• Hands-on Labs:
  • Building a scalable Azure architecture, including auto-scaling and load balancing, optimized with AI insights
  • Applying machine learning models to forecast workload and configure scaling parameters for cost efficiency

Part 3: Managing and Monitoring Azure Cloud Infrastructure Using AI

Module 5: AI-Enhanced Management and Monitoring with Azure Tools

• Topics Covered:
  • Monitoring with Azure Monitor, Azure Log Analytics, and setting up AI-based insights
  • Real-time data monitoring and anomaly detection using Azure Cognitive Services
  • Setting up automated alerts and actions for resource utilization and issue detection
• Hands-on Labs:
  • Configuring Azure Monitor dashboards, log analytics, and AI-based alerts
  • Practicing anomaly detection using AI models with data from Azure Monitor

Module 6: Security and Compliance in AI-Driven Azure Cloud Management

• Topics Covered:
  • Azure security principles, compliance requirements, and AI-driven security features
  • Using Azure Security Center, Sentinel, and Machine Learning to detect vulnerabilities
  • Implementing AI-based intrusion detection and response in Azure
• Hands-on Labs:
  • Configuring and using Azure Security Center and Sentinel for security audits with AI analysis
  • Implementing AI-driven intrusion detection and configuring automatic response policies

Part 4: Troubleshooting and Optimizing Azure Cloud Infrastructure with AI

Module 7: Troubleshooting Azure Environments with AI-Powered Tools

• Topics Covered:
  • Identifying and resolving common Azure issues using AI-based diagnostics and root cause analysis
  • Applying predictive analytics for proactive troubleshooting
  • Using AI to resolve performance bottlenecks, reduce latency, and improve system stability
• Hands-on Labs:
  • Setting up an AI-driven troubleshooting workflow for Azure resources using Azure Monitor and Machine Learning models
  • Practicing root cause analysis and predictive troubleshooting exercises in Azure