AI Optimization & Automation in Cloud Environments

 

About the Course

This advanced hands-on workshop introduces participants to the power of AI-driven automation and optimization in modern cloud infrastructures.
Using Azure AI, Machine Learning, and Cognitive Services, participants will learn how to improve performance, reliability, and cost efficiency across large-scale environments.

The course focuses on implementing AIOps (AI for IT Operations) using Azure’s open ecosystem from data collection to predictive analysis and automated remediation.

Target Audience

Cloud Architects, DevOps Engineers, System Administrators, and Data Engineers who manage or monitor large-scale Azure environments and want to leverage AI for smarter operations.

Prerequisites

  • Basic understanding of Azure Monitor and Log Analytics

  • Familiarity with Azure infrastructure and scripting (PowerShell, Python, or Bash)

Topics Covered

1. Introduction to AI-Driven Cloud Optimization

  • What is AIOps and where it fits in modern infrastructure

  • Key Azure services for intelligent automation

  • Use cases: predictive scaling, self-healing systems, and automated cost control

2. Building the Optimization Framework

  • Data collection and correlation using Azure Monitor, Log Analytics, and App Insights

  • Connecting AI models to telemetry and operational data

  • Creating anomaly detection and root-cause models in Azure Machine Learning

3. AI-Powered Automation

  • Integrating Logic Apps, Azure Functions, and Power Automate for automated responses

  • Implementing predictive alerts and self-remediation pipelines

  • Example: AI identifies and fixes performance degradation before users notice

4. Cloud Cost & Resource Optimization

  • Using AI for dynamic scaling and budget forecasting

  • Detecting idle workloads and waste using telemetry data

  • Connecting to FinOps dashboards for actionable cost insights

5. Integration with DevOps & Monitoring Tools

  • Connecting to Azure DevOps, GitHub, and ServiceNow workflows

  • Automatically opening tickets and tracking anomalies

  • Using OpenAI to summarize incidents or logs and propose solutions

6. Hands-On Labs

  • Build an AI model that predicts CPU or memory anomalies

  • Create an auto-healing pipeline that restarts affected services

  • Visualize insights using Power BI and Application Insights dashboards

Learning Outcomes

By the end of the course, participants will be able to:

  • Design AI-based optimization workflows for cloud infrastructure

  • Implement predictive monitoring and automated resolution pipelines

  • Integrate AIOps models across DevOps and observability platforms

Key Takeaways

  • Azure AI in practice for real-world automation

  • End-to-end AIOps implementation

  • Improved stability, lower cost, and faster response times