What does the training include?
This training gives you a practical insight into how AI systems are structured. You'll learn how large language models (LLMs) work, how they combine context and knowledge via Retrieval-Augmented Generation (RAG), and how to create AI agents with specific knowledge and instructions. The sessions cover prompt engineering, embeddings, vector databases and multi-agent architectures through practical exercises.
Day program
Day 1 — Introduction to Agents
Learn the basics of AI and language models, discover the concept of agents, and build your first simple agent.
Day 2 — Multi-Agent Systems
Understand how multiple agents can work together on complex tasks and build your own multi-agent workflow.
Day 3 — Alternate Frameworks
Get to know frameworks like LangChain and LangGraph for building flexible and scalable agent architectures.
Day 4 - Implementation and Best Practices
Learn how to deploy agents in ETL or development environments, and apply best practices for reliability, scalability, and performance.
Day 5 — Case
Work on a complete end-to-end case where you'll apply all the concepts you've learned to design and implement an advanced AI agent solution.
For whom?
This training is intended for data scientists, data engineers and AI professionals who already have some experience with Python and AI concepts and want to deepen their knowledge towards practical, production-grade AI applications.
