Kartik Nighania

Learn how to train LLMs with HuggingFace and use RAG with Qdrant 

Deploy and create automated pipelines for LLMs with SageMaker

Build multi-agent workflows with LangGraph

How to test, monitor, and evaluate LLMs with LangSmith 

Through practical labs, participants will build production-ready LLM systems incorporating best practices for scalability and reliability.

Prerequisite: Basic Python programming skills, basic understanding of machine learning concepts, and familiarity with AWS services.

*Note: These are tentative details and are subject to change.

This hands-on session reveals battle-tested strategies for scaling AI agents from prototype to production. We'll cover critical engineering practices including robust monitoring systems, comprehensive logging frameworks, automated testing pipelines, and CICD workflows optimized for agent deployments. Participants will learn concrete techniques to detect hallucinations, measure reliability metrics, and implement guardrails that ensure consistent agent performance under real-world conditions. Join us for practical insights on building GenAI systems that don't just work in demos, but deliver dependable value in production environments.