Building Production-Ready AI Systems

12:30 dop. - 1:30 dop. (UTC)

Modern AI systems require more than powerful models—they require security, evaluation, governance, and continuous improvement. This session combines lessons from production AI agent security with real-world LLM evaluation and fine-tuning workflows. Topics may include prompt injection, tool abuse, memory poisoning, defense-in-depth architectures, custom evaluation frameworks, Azure OpenAI fine- tuning, and practical engineering lessons learned from deploying AI-powered systems. Key Takeaways: Understand security challenges in AI agents Learn practical defense patterns for production AI Explore LLM evaluation methodologies Understand fine-tuning workflows using Azure OpenAI Apply production engineering best practices to AI systems

12:30 dop. - 1:30 dop. (UTC)

AI applications depend on scalable and reliable data platforms. This session explores how Microsoft Fabric enables modern data engineering workflows through lakehouse architecture, data ingestion, orchestration, Spark-based processing, analytics, and governance. Attendees will learn how to build strong data foundations to support analytics, machine learning, and generative AI workloads. Key takeaways: Modern lakehouse architecture Building scalable data pipelines Spark and Fabric integration Data engineering best practices for AI workloads

12:30 dop. - 1:30 dop. (UTC)

AI-assisted development is transforming how engineers build and maintain software. This session showcases practical workflows using GitHub Copilot and VS Code, covering code generation, debugging, testing, documentation, and productivity techniques you can apply in your day-to-day work. Attendees will learn how to enhance their development process with AI while maintaining efficiency, quality, and responsible practices. Key takeaways: Effective prompting for software development AI-assisted debugging and testing Developer productivity workflows Responsible AI usage practices