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Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

  • Large Language Models (LLMs) Overview: Gaining insight into their potential and practical applications. 
  • Getting Started with AutoGen v0.4: Investigating its key features, structural design, and the ways it streamlines the creation of agentic AI systems.

2. Fundamental Concepts and Components of AutoGen

  • Comprehending the Layered Framework:
    • Core Layer: An event-driven architecture that supports dynamic workflow management.
    • AgentChat API: Enabling the creation of task-oriented agents using intuitive high-level APIs.
    • Extensions: Connecting custom agents, external tools, and memory modules to expand system capabilities.
  • Asynchronous Messaging: Deploying event-driven and request-response interaction patterns. 

3. Developing Your First Multi-Agent Application

  • Agent Definition: Establishing Assistant and User Proxy agents. 
  • Setting Up Agent Communication: Configuring asynchronous messaging channels between agents. 
  • Sample Application Implementation: Constructing a basic multi-agent system designed to execute a specific objective. 
  • Observability and Debugging Tools: Leveraging native metric tracking and message tracing features for real-time system monitoring. 

4. Case Studies and Best Practices

  • Real-World Applications: Analyzing successful AutoGen deployments across different sectors.
  • Best Practices: Strategic guidelines for engineering efficient and scalable LLM applications via AutoGen.
  • Challenges and Solutions: Tackling typical development obstacles and exploring effective resolutions.
  • Q&A Session

This workshop is ideal for:

  • software developers
  • data scientists
  • data engineers
  • individuals with programming experience or interest who wish to delve into AI programming.

Requirements

Prerequisites - Proficiency in Python programming

 7 Hours

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