Get in Touch

Course Outline

1. Introduction to LLM Applications and AutoGen v0.4

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

2. Core Concepts and Components of AutoGen

  • Understanding the Layered Framework:
    • Core Layer: Event-driven architecture supporting dynamic workflows.
    • AgentChat API: Constructing task-focused agents using high-level APIs.
    • Extensions: Integrating custom agents, tools, and memory modules to boost functionality.
  • Asynchronous Messaging: Implementing event-driven and request-response interaction models. 

3. Building Your First Multi-Agent Application

  • Defining Agents: Creating Assistant and User Proxy agents. 
  • Establishing Agent Communication: Setting up asynchronous messaging between agents. 
  • Implementing a Sample Application: Developing a basic multi-agent system to address a specific task. 
  • Observability and Debugging Tools: Utilizing built-in metric tracking and message tracing for real-time monitoring. 

4. Case Studies and Best Practices

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

The workshop is intended for:

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

Requirements

Prerequisites - Python programming

 7 Hours

Number of participants


Price per participant

Testimonials (5)

Upcoming Courses

Related Categories