SWARC4AI (Online) - Tobias Scholz - German
Take your software architecture skills to the next level and learn how to develop robust, customizable architectures for ML and AI systems.
In our practice-oriented training course “Software Architecture for AI and ML Systems (SWARC4AI)”, you will learn how to successfully integrate ML components into modern software architectures. You will learn what is important when designing long-lasting, scalable and secure architectures for AI-supported systems - from data acquisition and processing to the use of LLMs, operation and further development.
The training is aimed at software architects and developers who have already gained initial experience with machine learning and now want to learn how to embed ML systems sustainably in software architectures.
Our SWARC4AI training is not a frontal event - you will work intensively on practical case studies. In small teams, you will formulate ML problems, prepare data, develop features, select models and train or adapt them. You will exchange ideas with other participants, discuss possible solutions and benefit from different perspectives. Our trainers support you with impulses, questions and assistance - for independent and sustainable learning.
Agenda
- Integration of machine learning systems into existing software architectures
- Designing architectures for data acquisition, processing, persistence and utilization
- Dealing with architectural requirements such as scalability, maintainability and ethical responsibility
- Overview of different ML and LLM models and their impact on architecture
- Recognizing and dealing with problems such as model drift or data bias
- Integration of LLMs into architecture concepts
- Legal, ethical and security-related aspects when using AI systems
Your Trainers
Tobias Scholz
INNOQ
AI Architectures, Agentic Engineering, Testing Concepts
- Designing software architectures for AI and ML systems
Tobias is a Senior Consultant at INNOQ and helps companies design and implement resilient software systems—from architectural decisions to production-ready implementation. Drawing on his extensive technical experience, he guides teams in developing sustainable solutions and building the necessary expertise to maintain them independently. His current areas of focus include AI architectures, agentic engineering, and testing strategies.