AI Programming for Experts

Generative AI has left the experimental phase and moved into the productive corporate world. However, successful use beyond simple prompting requires technical understanding. Standard models often reach their limits. In order to achieve a real gain, architectures often have to be designed in-house, models adapted or complex workflows automated.

In this AI workshop, you will gain the skills to develop customized AI solutions. You will immerse yourself in the architecture of modern language models and learn how to operate open source models locally and optimize them efficiently.

The gap between theory and practice is closed with the help of numerous practical exercises such as building a RAG pipeline or a multi-agent system.

• Understanding & Implementing AI Architecture
• Transformers, self-attention, and a comparison of classical ML and LLMs in practice.
• Mastering Open-Source LLMs – Hands-On with Llama, Qwen, and Mistral; Deployment with Ollama and vLLM; Understanding Licensing Models and Building Core Components Yourself with PyTorch
• Targeted Model Specialization – Parameter-Efficient Fine-Tuning with LoRA & QLoRA, Behavior Control via RLHF and DPO, MLOps with Hugging Face & MLflow
• Intelligent Knowledge Integration – Production-Ready RAG Pipelines with Langchain and LlamaIndex, Hybrid Search, Graph RAG, and Systematic Evaluation with RAGAs
• Developing Autonomous AI Systems – Multi-Agent Architectures with LangGraph/Autogen/CrewAI, Tool Usage, Hierarchical Teams, and the Model Control Protocol (MCP) 

This AI training course is designed for developers and data scientists who want to master the leap to modern AI technologies.

It is also perfect for anyone who wants to drive the next generation of AI solutions in their company as an AI expert and needs a sound technical understanding.

You are confident in programming in Python and have already had your first contact with machine learning. You can acquire this knowledge in the following courses:

Machine Learning - Data Science and Artificial Intelligence

MLOps workshop for programmers - practical use cases

AI Programming Advanced - Agentic AI and API Use Cases

First experience with deep learning (e.g. through PyTorch, Tensorflow or with Hugging Face) is recommended. You don't have to be an expert, but you should be familiar with how machine learning works

A basic understanding of the special features of large language models is helpful and can be acquired in the following courses, for example:

Using AI tools and LLMs successfully - ChatGPT, Gemini, Claude & Co.

AI Programming Advanced - Agentic AI and API Use Cases

You will gain a deep understanding of the latest AI technologies and become an architect of autonomous AI systems. You will master the basics of transformer architecture, agentic AI as well as the principles of fine tuning large language models and the development of multi-agent systems.

You will acquire the skills to implement modern LLMs with PyTorch, operate them with vLLM and Ollama and optimize them with LoRA/QLoRA. You will be able to develop production-ready RAG pipelines and autonomous multi-agent systems.
In addition, you will gain the skills to analyze, structure and solve more complex AI problems in practice.

Using AI tools and LLMs successfully - ChatGPT, Gemini, Claude & Co.

AI Programming Advanced - Agentic AI and API Use Cases