Target Audience

• DevOps Engineers
• IT Engineers
• Platform Engineers
• Site Reliability Engineers (SREs)
• Automation and Infrastructure Engineers
• Technical Leads responsible for operational tooling

Description

AI is rapidly becoming a core capability for DevOps and IT teams, extending far beyond chat-based assistants. Modern AI systems can analyze logs, assist in CI/CD pipelines, validate infrastructure, and support incident response, when they are properly engineered and grounded in real operational context.

This workshop provides a practical and structured introduction to Machine Learning concepts, Prompt Engineering, Context Engineering, and agent-based architectures using MCP. You will gain hands-on experience designing and operating MCP agents and MCP servers, working with local Large Language Models (LLMs), and applying AI to real-world DevOps and IT use cases.

Objectives

At the end of the workshop, you will be able to:

• Understand core Machine Learning and LLM concepts relevant to DevOps and IT
• Apply Prompt Engineering techniques for operational and engineering tasks
• Apply Context Engineering to reduce hallucinations and improve accuracy
• Design and operate MCP agents for real-world automation scenarios
• Understand MCP server architecture and tool exposure
• Work with local LLMs for secure and cost-controlled environments
• Apply MCP-based AI agents to CI/CD, log analysis, incident response, and infrastructure validation

Main Topics

• Introduction to Machine Learning for Engineers
• Prompt Engineering
• Context Engineering
• MCP Agents
• MCP Servers
• Working with Local LLMs
• Applied DevOps Use Cases
• Log analysis
• Incident summarization
• Infrastructure validation
• CI/CD assistance

Key Learning Outcomes

At the end of the workshop, you will have hands-on experience using one of the following AI-enabled environments (based on organizational preference):

• Cursor – AI-powered IDE for MCP agent interaction and context-driven workflows
• Visual Studio Code – Extended with AI capabilities for MCP-based engineering tasks
• Claude Code – Terminal-based environment for agent execution and reasoning
• Gemini Anti-Gravity – Cloud-based AI environment for advanced MCP-enabled workflows

You will be able to:

• Build and run MCP agents
• Design MCP server tools and execution boundaries
• Engineer prompts and structured context
• Analyze logs and incidents using MCP-based agents
• Assist CI/CD pipelines and validate infrastructure using AI