Course
digicode: LLMENG
LLM Engineering & AIOps – Overview and Applications
Course facts
Download as PDF- Understanding AI System Architecture: Integrating LLMs into Modern IT Infrastructure
- Model & Infrastructure Strategy: Differentiating Between Proprietary, Open-Weight, and Open-Source Models
- Enterprise Integration: Connecting LLMs, RAG (Retrieval Augmented Generation), and MCP (Model Context Protocol) with Applications and Enterprise Knowledge
- Scalable Automation: Understanding the Architecture of Multi-Agent Systems and Their Use in Complex Automation Processes
In this course, you’ll gain a structured understanding of modern AI systems with a focus on large language models (LLMs). You’ll learn to understand these technologies and how to integrate them effectively into your platform.
1 Fundamentals & Model Ecosystem
- Explore the evolution from traditional machine learning to modern foundation models.
- Compare open-source and proprietary models and understand the relationship between model size and performance.
- Identify the right use cases for your business.
2 Hands-On Implementation & Infrastructure
- Learn how to find and evaluate models and master the difference between training and inference.
- Understand the specific infrastructure requirements for stable operation.
- Weigh the pros and cons of cloud-based models versus self-hosted models.
3 Integration (MCP) & Enterprise Data (RAG)
- Access enterprise knowledge and integrate it into existing LLMs (Large Language Models).
- Connect LLMs to your databases and tools using modern protocols such as MCP.
- RAG vs. LLM fine-tuning vs. LoRA (Low-Rank Adaptation).
4 Multi-Agent Systems in the Enterprise
- Build multi-agent systems in which specialized agents work together to solve complex tasks.
- Keep operational aspects and scalability under control.
- Seamlessly integrate your solutions into existing IT infrastructures.
The course is designed to be interactive and combines demonstrations, hands-on exercises, and theoretical insights based on a sample project and cloud-native concepts. Participants will learn about typical operational requirements and apply selected tools directly in the lab environment.
- DevOps and Platform Engineers
- Site Reliability Engineers (SREs)
- Software Developers with a Focus on Developer Experience
- Technical Project Managers and Architects
Course participants should be able to read simple Python scripts and have a basic understanding of containers and Kubernetes (e.g., the DUK course).