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iSAQB® SWARC4AI - Advanced Level Certification
Advanced
The iSAQB Advanced Level SWARC4AI (Software Architecture for AI Systems) course teaches software architects how to design modern architectures for AI and machine learning systems. Participants learn how to integrate AI components, data pipelines, and machine learning models into scalable and maintainable software architectures.
Upcoming SWARC4AI Sessions
3 sessionsDescription
The iSAQB Advanced Level SWARC4AI (Software Architecture for AI Systems) course teaches software architects how to design modern architectures for AI and machine learning systems. Participants learn how to integrate AI components, data pipelines, and machine learning models into scalable and maintainable software architectures.
The training focuses on key topics such as AI system design, model integration, data management, and operational reliability. It also covers architectural decisions that support secure, transparent, and compliant AI solutions.
After the course, participants understand how to design robust software architectures for AI-driven applications and manage the lifecycle of AI components in complex IT environments.
What You Will Learn
The iSAQB Advanced Level SWARC4AI (Software Architecture for AI Systems) course covers the architectural principles and methods required to design and operate AI based software systems.
Introduction to AI System Architecture
Understanding how AI systems differ from traditional software systems and what architectural challenges arise when integrating machine learning.
AI Components and System Design
Designing architectures that include machine learning models, data pipelines, and AI services.
Data Management for AI Systems
Managing data collection, preparation, and data flows that support machine learning models.
Model Integration and Deployment
Integrating trained models into software systems and managing model deployment and updates.
Operational Aspects of AI Systems
Monitoring, maintaining, and improving AI systems in production environments.
Quality Attributes for AI Architectures
Addressing performance, reliability, scalability, and security in AI based systems.
Governance and Responsible AI
Understanding transparency, accountability, and regulatory considerations such as AI governance requirements.
Architectural Patterns for AI Applications
Exploring common architecture patterns used in AI enabled software systems.
Certification & Exam
After successful participation in an iSAQB-accredited SWARC4AI training, you will receive a certificate of participation with 20 credit points in technical competence and 10 credit points in methodological competence. You can count these points toward CPSA-A certification. SWARC4AI is not an independent certificate, but an Advanced Level module within the iSAQB certification system.
To qualify for the CPSA-A certification exam, you need at least 70 credit points in total, with at least 10 points each in technical, methodological, and communicative areas, as well as the other iSAQB requirements, including the CPSA-F certificate. You can find details at the official iSAQB source on SWARC4AI.
What You Will Achieve
After the training, you can:
Plan how AI-based system architectures for traditional AI and generative AI are structured.
Analyze which requirements, risks, and quality characteristics are relevant for AI systems.
Evaluate how uncertainty, black-box behavior, data dependencies, and model limitations influence architectural decisions.
Consider which technical and regulatory requirements, including the EU AI Act, play a role in AI systems.
Apply architectural patterns that are suitable for AI-based systems and generative AI.
Integrate how MLOps is integrated into modern system architectures, for example for training, deployment, monitoring, and operation of models.
Design how AI solutions are integrated into existing IT environments, data flows, and applications.
Training Providers
1 providerFAQs
SWARC4AI is an Advanced Level module called Software Architecture for AI Systems. It teaches software architecture concepts for building AI systems, including machine learning and generative AI, and how to integrate AI parts into classic software systems.
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