Overview
This iSAQB module SWARC4AI focuses on software architecture for AI systems. The three day program introduces how modern AI technologies can be integrated into scalable, auditable and secure software architectures. The module emphasizes data governance, MLOps, and generative AI, with a strong methodological toolkit to structure complex requirements and support sustainable architectural decisions. Participants learn how to design architectures that enable AI components such as machine learning models and generative AI to operate cohesively within broader software systems. The course blends theory with hands-on practice, ensuring attendees can translate concepts into real-world solutions that meet quality attributes like scalability, explainability, and safety.
Target audience
The module is designed for professionals involved in software architecture, AI integration, data engineering, and related roles who want a solid foundation in architecting AI enabled systems. It is suitable for those preparing for advanced level certifications and seeking credits toward iSAQB requirements.
Format and agenda
SWARC4AI is delivered as a structured three-day training. The agenda covers a comprehensive sequence of topics, including foundational AI concepts relevant to software architecture, differences between AI systems and traditional software, and compliance with security and privacy requirements. It includes design patterns for AI systems, integration of machine learning into existing architectures, and data management strategies such as data pipelines and data engineering tools. The program also features operating characteristics for running AI systems, including scalability, monitoring, and MLOps practices, as well as approaches to Generative AI such as LLMs and retrieval augmented generation (RAG). Real-world case studies and hands-on exercises enable participants to apply learned patterns to their own projects.
Learning outcomes and credits
Participants will gain a structured understanding of how to design AI-enabled software architectures, focusing on scalability, security, and explainability. The module assigns credits across methodology and technology: typically 10 methodical credits and 20 technical credits, aligned with the iSAQB curriculum. Attendees will be able to articulate architectural decisions, evaluate risks, and plan for compliance with relevant regulations (e.g., EU AI Act, data privacy requirements). The course also emphasizes documentation practices for architectural decisions and the creation of artifact templates that can be reused across projects.
Prerequisites and prerequisites alignment
Participants should have baseline knowledge in machine learning, data processing, and software architecture to maximize the benefits of the module. These prerequisites help ensure that attendees can immediately apply the concepts presented and translate them into project work.
What you will take away
A clear understanding of how to combine AI technologies with conventional software architecture practices to create robust, scalable and auditable systems. You will learn architectural patterns for AI integration, data governance considerations, and the role of governance, security, and compliance in AI deployments. The training prepares you to design, document, and implement AI capable architectures in real-world environments and to pursue further certification within the iSAQB framework.
FAQ
This module forms part of the Advanced Level program and offers a roadmap to further specialization in AI oriented architecture. If you need to know more about specific speakers, exact session timings, or venue logistics, please consult the event organizers for the latest schedule and details.
