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Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

  • Overview of Large Language Models (LLMs): Grasping their capabilities and potential applications. 
  • Introduction to AutoGen v0.4: Investigating its key features, architectural design, and how it streamlines the development of agentic AI systems.

2. Core Concepts and Components of AutoGen

  • Understanding the Layered Framework:
    • Core Layer: An event-driven architecture designed to support dynamic workflows.
    • AgentChat API: Facilitating the creation of task-oriented agents via high-level APIs.
    • Extensions: Enabling the integration of custom agents, tools, and memory modules to expand functionality.
  • Asynchronous Messaging: Executing event-driven and request-response interaction patterns. 

3. Building Your First Multi-Agent Application

  • Defining Agents: Establishing Assistant and User Proxy agents. 
  • Establishing Agent Communication: Configuring asynchronous messaging protocols between agents. 
  • Implementing a Sample Application: Constructing a basic multi-agent system to resolve a specific task. 
  • Observability and Debugging Tools: Leveraging built-in metric tracking and message tracing for real-time monitoring. 

4. Case Studies and Best Practices

  • Real-World Applications: Analyzing successful AutoGen deployments across various industries.
  • Best Practices: Guidelines for designing efficient and scalable LLM applications using AutoGen.
  • Challenges and Solutions: Tackling common development hurdles and their respective resolutions.
  • Q&A

This workshop is intended for:

  • software developers
  • data scientists
  • data engineers
  • individuals with a programming background or interest who wish to learn AI programming.

Requirements

Prerequisites - Python programming

 7 Hours

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