tecnovy GmbH
Online / Onsite
Training
The iSAQB® SWARC4AI training provides the knowledge and skills needed to design software architectures specifically for Artificial Intelligence (AI) applications.
Participants learn about the architectural challenges of AI systems and how to efficiently integrate them into software solutions. The training covers topics such as data pipelines, scalability, model deployment, as well as ethical and security considerations in AI applications.
The syllabus was developed with regard to the syllabus of iSAQB e.v. (version 2024.1) published and consists of the following six parts.
Part 1-Introduction to concepts relevant to software architecture for artificial intelligence
Part 2-Compliance, security, alignment
Part 3-Design and development of AI systems
Part 4-Data management and data processing for AI systems
Part 5-Important quality features for the operation of AI systems
Part 6-System architectures and platforms for generative AI systems
Part 7-Case studies and practical projects
No sessions available.
After you complete an accredited SWARC4AI training, you receive 20 credit points in the technological competence area and 10 credit points in the methodical area. These points count toward the iSAQB CPSA-A certification.
To become eligible for the full CPSA-A certification you must:
Already hold the CPSA-F (Foundation Level) certificate.
Have at least three years of full-time professional experience in IT, including participation in the design and development of at least two different IT systems.
Accumulate a total of at least 70 credit points across the three competence areas (Technological, Methodical, Communicative), with at least 10 credit points in each area.
After you meet those prerequisites and have accumulated enough points (including those from the SWARC4AI module), you register for the CPSA-A final certification exam (which is an assignment plus an oral interview with iSAQB-appointed examiners).
Once you pass the exam, you receive the CPSA-A certificate, confirming your advanced competence in software architecture.
After the training, you will be able to:
Understand and plan AI-based system architectures
Analyze typical requirements and risks of AI systems
Evaluate uncertainty, black-box behavior, and data dependencies
Address technical and regulatory demands, including the EU AI Act
Apply architectural patterns for traditional and generative AI
Integrate MLOps into modern system architectures
Design solutions for integrating AI into existing environments
Online / Onsite
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