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Course Outline

1. Fundamentals of LLM Applications and AutoGen v0.4

  • Large Language Models (LLMs): A look at their core capabilities and diverse applications. 
  • AutoGen v0.4 Overview: An examination of its features, architecture, and how it streamlines the creation of agentic AI systems.

2. Essential Concepts and Components of AutoGen

  • The Layered Framework Explained:
    • Core Layer: An event-driven architecture designed to support dynamic workflows.
    • AgentChat API: A high-level interface for constructing task-oriented agents.
    • Extensions: Incorporating custom agents, tools, and memory modules to expand functionality.
  • Asynchronous Messaging: Adopting event-driven and request-response interaction patterns. 

3. Creating Your First Multi-Agent Application

  • Agent Definition: Setting up Assistant and User Proxy agents. 
  • Agent Interaction: Configuring asynchronous messaging channels between agents. 
  • Sample Implementation: Building a straightforward multi-agent system to address a specific objective. 
  • Monitoring and Troubleshooting: Leveraging built-in metric tracking and message tracing for live oversight. 

4. Case Studies and Industry Best Practices

  • Practical Applications: Reviewing successful AutoGen deployments across different sectors.
  • Best Practices: Recommendations for architecting efficient and scalable LLM applications with AutoGen.
  • Challenges and Remedies: Tackling common development hurdles and providing effective solutions.
  • Q&A

This workshop is suitable for:

  • software developers
  • data scientists
  • data engineers
  • individuals with a programming background or strong aptitude who wish to explore AI programming.

Requirements

Prerequisites - Python programming

 7 Hours

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