In this jam-packed, one-day lecture and demo-focused workshop, Brian Randell, Mickey Goussett, and Ken Muse will take you on a journey through building intelligent AI-powered applications using C#, GitHub Copilot, Azure AI services, and modern DevOps practices. You’ll explore essential AI concepts, practical architectural patterns, and best practices for integrating powerful AI models into your applications. Join us as they demonstrate state-of-the-art techniques, including Retrieval-Augmented Generation (RAG), semantic search, advanced prompt engineering, and responsible AI practices. You’ll also see how DevOps, MLOps, and Platform Engineering come together seamlessly, enabling secure, reliable, and continuous deployment of intelligent applications.

This focused workshop provides clear guidance, live demonstrations, and actionable knowledge—perfect for developers who want a comprehensive overview and practical insights.

Welcome & AI Fundamentals This module provides an introduction to the transformative power of Large Language Models (LLMs) and essential AI tools available for developers. You’ll see live demonstrations of GitHub Copilot enhancing coding productivity directly within Visual Studio 2022, Visual Studio Code, and github.com.

The AI Revolution: How LLMs are reshaping software engineering
Overview of key AI tools: GitHub Copilot, Azure OpenAI Service, Semantic Kernel
An overview of self-hosted options for running your LLMs

Architectural Patterns for AI-Enhanced Apps Explore practical architectural patterns crucial for integrating AI capabilities into modern applications. You’ll learn about key components like LLM services, embedding models, vector databases, and the powerful Retrieval-Augmented Generation (RAG) technique.

Patterns overview: LLM services, embeddings, vector databases, semantic search
Introduction to Retrieval-Augmented Generation (RAG)
Memory and state management for conversation history

Practical Prompt Engineering This module covers the principles of effective prompt engineering to ensure accurate and reliable results from AI models. You’ll learn techniques like crafting clear system and user prompts, employing few-shot examples, and leveraging function-calling capabilities.

Crafting effective prompts for accurate results
System vs. user prompts, few-shot prompting, and function calling

DevOps Meets MLOps Learn how modern DevOps practices integrate seamlessly with Machine Learning Operations (MLOps) to enhance AI-driven development. The team will showcase continuous integration and automated workflows specifically tailored to deploying intelligent applications with examples using GitHub Actions.

Integrating AI and ML into modern DevOps pipelines
Continuous integration for AI-driven projects
Continuous deployment for AI-driven projects

Advanced AI Techniques and Agentic Workflows This module dives deeper into advanced AI capabilities, exploring frameworks that enable agents to handle complex, multi-step tasks. You’ll discover how chained reasoning, state management, and tool integration can produce powerful AI workflows. We will also cover MCP (Model Context Protocol) servers, innovative technology that enables AI models to interact with external tools, data sources, and services.

Exploring agent frameworks and chained reasoning
Tool integration, planners, and state management in AI agents
Agentic workflows with MCP Servers

Securing AI Applications with GitHub Advanced Security Security considerations unique to AI-driven applications are highlighted here, emphasizing how GitHub Advanced Security (GHAS) helps maintain secure codebases. Brian will show how GHAS can help you produce more secure code and how it works with Copilot to help you build better solutions.

Security challenges unique to AI-driven codebases
Leveraging GHAS for automated vulnerability detection and remediation

Responsible AI and Data Privacy This section addresses responsible AI practices, ethical considerations, and essential privacy protections necessary for AI applications. We will demonstrate practical methods for implementing content moderation and data privacy controls.

Content moderation and filtering in AI-driven apps
Best practices for ethical AI usage and privacy preservation
Azure content-filtering options

Deploying AI-Powered Applications to Azure Conclude the day by exploring deployment strategies for AI applications, weighing the pros and cons of Azure-managed versus self-hosted options. Practical demonstrations will show you how to deploy and monitor a live, production-grade AI-powered application on Azure.

Choosing Azure vs. self-hosted deployments
Practical deployment options: Azure App Service, Containers, Azure Functions
Hybrid solutions using the cloud and edge computing

You will learn:

Clearly understand essential AI development concepts and architectural patterns.
Practical techniques to integrate AI into your development workflows effectively.
Grasp modern DevOps and MLOps practices tailored for secure, AI-driven application delivery.
See real-world, production-grade AI applications deployed to Azure and secured through GitHub Advanced Security.