Curriculum forCertified Professional forSoftware Architecture (CPSA)®Advanced Level: ModuleSWARC4AISoftware Architecture for AI Systems

© (Copyright), International Software Architecture Qualification Board e. V. (iSAQB^® e. V.) 2024

The curriculum may only be used subject to the following conditions:

You wish to obtain the CPSA Certified Professional for Software Architecture Foundation Level^® certificate or the CPSA Certified Professional for Software Architecture Advanced Level^® certificate. For the purpose of obtaining the certificate, it shall be permitted to use these text documents and/or curricula by creating working copies for your own computer. If any other use of documents and/or curricula is intended, for instance for their dissemination to third parties, for advertising etc., please write to info@isaqb.org to enquire whether this is permitted. A separate license agreement would then have to be entered into.
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If you fall neither into category 1 nor category 2, but would like to use these documents and/or curricula nonetheless, please also contact the iSAQB e. V. by writing to info@isaqb.org. You will then be informed about the possibility of acquiring relevant licenses through existing license agreements, allowing you to obtain your desired usage authorizations.

Important Notice

We stress that, as a matter of principle, this curriculum is protected by copyright. The International Software Architecture Qualification Board e. V. (iSAQB^® e. V.) has exclusive entitlement to these copyrights.

The abbreviation "e. V." is part of the iSAQB’s official name and stands for "eingetragener Verein" (registered association), which describes its status as a legal entity according to German law. For the purpose of simplicity, iSAQB e. V. shall hereafter be referred to as iSAQB without the use of said abbreviation.

List of Learning Goals

LG 1-1: This the first learning goal, in category xy
LG 2-1: TBD
LG 2-2: TBD
LG 3-1: TBD
LG 3-2: TBD
LG 4-1: TBD
LG 4-2: TBD
LG 5-1: TBD
LG 5-2: TBD

Introduction: General information about the iSAQB Advanced Level

What is taught in an Advanced Level module?

The iSAQB Advanced Level offers modular training in three areas of competence with flexibly designable training paths. It takes individual inclinations and priorities into account.
The certification is done as an assignment. The assessment and oral exam is conducted by experts appointed by the iSAQB.

What can Advanced Level (CPSA-A) graduates do?

CPSA-A graduates can:

Independently and methodically design medium to large IT systems
In IT systems of medium to high criticality, assume technical and content-related responsibility
Conceptualize, design, and document actions to achieve quality requirements and support development teams in the implementation of these actions
Control and execute architecture-relevant communication in medium to large development teams

Requirements for CPSA-A certification

Successful training and certification as a Certified Professional for Software Architecture, Foundation Level^® (CPSA-F)
At least three years of full-time professional experience in the IT sector; collaboration on the design and development of at least two different IT systems
- Exceptions are allowed on application (e.g., collaboration on open source projects)
Training and further education within the scope of iSAQB Advanced Level training courses with a minimum of 70 credit points from at least three different areas of competence
Successful completion of the CPSA-A certification exam

Certified Professional for Software Architecture<sup>®</sup> Advanced Level (CPSA-A)

Essentials

What does the module “SWARC4AI” convey?

The module presents SWARC4AI to the participants … At the end of the module, the participants know … and are able to …

Curriculum Structure and Recommended Durations

Content	Recommended minimum duration (minutes)
1. Introduction	180
2. xz	150
3. Lots of theory	120
4. xy and example	180
5. abc und d	210
6. Final example	120

Total	960 (16h)

Duration, Teaching Method and Further Details

The times stated below are recommendations. The duration of a training course on the SWARC4AI module should be at least 3 days, but may be longer. Providers may differ in terms of duration, teaching method, type and structure of the exercises, and the detailed course structure. In particular, the curriculum provides no specifications on the nature of the examples and exercises.

Licensed training courses for the SWARC4AI module contribute the following credit points towards admission to the final Advanced Level certification exam:

Methodical Competence:

10 Points

Technical Competence:

20 Points

Communicative Competence:

0 Points

Prerequisites

Participants should have the following prerequisite knowledge:

Prerequisite 1
Prerequisite 2, etc.

Knowledge in the following areas may be helpful for understanding some concepts:

Area 1:
- Knowledge 1
- Experience 2
- Knowledge 3
- Experience 4
- Understanding 5

Structure of the Curriculum

The individual sections of the curriculum are described according to the following structure:

Terms/principles: Essential core terms of this topic.
Teaching/practice time: Defines the minimum amount of teaching and practice time that must be spent on this topic or its practice in an accredited training course.
Learning goals: Describes the content to be conveyed including its core terms and principles.

This section therefore also outlines the skills to be acquired in corresponding training courses.

Supplementary Information, Terms, Translations

To the extent necessary for understanding the curriculum, we have added definitions of technical terms to the iSAQB glossary and complemented them by references to (translated) literature.

1. This is the First Module’s Title

Duration: XXX min

Practice time: XXX min

1.1. Terms and Principles

Term 1, Term 2, Term 3

[Koc], [Dibia 2025], [Gradient Flow], [bornstein-radovanic], [Bahree 2024], [Spirin et al.], [Foster 2023], [Parnin]

7. And This is Module no 7

Duration: XXX min

Practice time: XXX min

7.1. Terms and Principles

Term 1, Term 2, Term 3

7.2. Learning Goals

References

This section contains references that are cited in the curriculum.

[Agrawal et al.] A. Agrawal, J. Gans, A. Goldfarb: Prediction Machines: The Simple Economics of Artificial Intelligence https://www.predictionmachines.ai/
[Alake] R. Alake: ML Pipeline Architecture Design Patterns (With 10 Real-World Examples) https://neptune.ai/blog/ml-pipeline-architecture-design-patterns
[ATLAS] ATLAS - Adversarial Threat Landscape for Artificial-Intelligence Systems. https://github.com/mitre/advmlthreatmatrix

[Bahree 2024] Bahree, A.: Generative AI in Action https://www.manning.com/books/generative-ai-in-action
[Bhajaria 2022] N. Bhajaria: Data Privacy - A runbook for engineers https://www.manning.com/books/data-privacy
[Bornstein et al.] M. Bornstein, J. Li, M. Casado: Emerging Architectures for Modern Data Infrastructure https://a16z.com/emerging-architectures-for-modern-data-infrastructure/
[bornstein-radovanic] M. Bornstein and R. Radovanovic: Emerging Architectures for LLM Applications https://a16z.com/emerging-architectures-for-llm-applications/
[Burkov 2019] Burkov, A.: The Hundred-Page Machine Learning Book https://themlbook.com/

[Cdteliot] AI Agents: Understanding Their Impact and Functions https://www.perplexity.ai/page/ai-agents-understanding-their-bL1Mg8FeStyUB4o9u3HT5Q
[Chen et al. 2022] C. Chen, N. R. Murphy, K. Parisa, D. Sculley, T. Underwood: Reliable Machine Learning https://www.oreilly.com/library/view/reliable-machine-learning/9781098106218/
[Chong et al.] J. Chong, Y. C. Chang: How to Lead in Data Science https://www.manning.com/books/how-to-lead-in-data-science
[Crowe et al. 2024] R. Crowe, H. Hapke, E. Caveness, D. Zhu: Machine Learning Production Systems https://learning.oreilly.com/library/view/machine-learning-production/9781098156008/
[CSIRO et al. 2023] CSIRO, Q. Lu, J. Wittle, X. Xu, L. Xhu: Responsible AI: Best Practices for Creating Trustworthy AI Systems https://www.oreilly.com/library/view/responsible-ai-best/9780138073947/

[Dehghani] Z. Dehghani: Data Mesh https://learning.oreilly.com/library/view/data-mesh/9781492092384/
[Dell’Acqua 2022] Fabrizio Dell’Acqua et al.: Paper: “Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality” https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf
[Dibia 2025] V. Dibia with C. Wang: Multi-Agent Systems with AutoGen https://www.manning.com/books/multi-agent-systems-with-autogen

[Engler et al.] M. Engler, N. Dhamani: Generative AI. Misuse and Adversarial Attacks. https://learning.oreilly.com/library/view/introduction-to-generative/9781633437197/OEBPS/Text/05.html
[EU AI Act] EU AI Act https://artificialintelligenceact.eu/de/ai-act-explorer/

[Ford et al.] N. Ford, M. Richards, P. Sadalage, Z. Dehghani Software Architecture: The Hard Parts. https://learning.oreilly.com/library/view/software-architecture-the/9781492086888/
[Foster 2023] D. Foster: Generative Deep Learning, 2nd Edition https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/

[Géron 2022] Aurélien Géron: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow https://learning.oreilly.com/library/view/hands-on-machine-learning/9781098125967/
[Gradient Flow] LLM Routers Unpacked https://gradientflow.com/llm-routers-unpacked/

[Hall et al. 2023] P. Hall, J. Curtis, P. Pandey: Machine Learning for High-Risk Applications https://www.oreilly.com/library/view/machine-learning-for/9781098102425/
[Harvard et al. 2024] Harvard Business Review, E. Mollick, D. De Cremer, T. Neeley, P. Sinha: Generative AI: The Insights You Need. (Generative AI Use Cases) https://learning.oreilly.com/library/view/generative-ai-the/9781647826406/
[Haviv et al. 2023] Y. Haviv, N. Gift: Implementing MLOps in the Enterprise https://www.oreilly.com/library/view/implementing-mlops-in/9781098136574/
[Heiland et al. 2023] L. Heiland, M. Hauser, J. Bogner: Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository. 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN). IEEE, 2023.
[Hotz, Best Practices] N. Hotz: 15 Data Science Documentation Best Practices https://www.datascience-pm.com/documentation-best-practices/
[Hotz, Life Cycle] N. Hotz: What is a Data Science Life Cycle? https://www.datascience-pm.com/data-science-life-cycle/
[Hotz, TDSP] N. Hotz: What is TDSP https://www.datascience-pm.com/tdsp/
[Huyen 2022] C. Huyen: Designing Machine Learning Systems https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/

[Jarmul 2023] K. Jarmul: Practical Data Privacy https://www.oreilly.com/library/view/practical-data-privacy/9781098129453/
[Jones] A. Jones: Driving Data Quality with Data Contracts https://learning.oreilly.com/library/view/driving-data-quality/9781837635009/

[Kelleher 2015] John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy: Fundamentals of Machine Learning for Predictive Data Analytics https://mitpress.mit.edu/9780262029445/fundamentals-of-machine-learning-for-predictive-data-analytics
[Koc] V. Koc: Generative AI Design Patterns: A Comprehensive Guide https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0
[Kumara et al.] I. Kumara, R., D. Di Nucci, W. J. Van Den Heuvel, D. A. Tamburri: Requirements and Reference Architecture for MLOps:Insights from Industry https://www.techrxiv.org/doi/full/10.36227/techrxiv.21397413.v1

[Lakshmanan et al.] V. Lakshmanan, S Robinson, M. Munn: Machine Learning Design Patterns https://learning.oreilly.com/library/view/machine-learning-design/9781098115777/

[Masood et al. 2023] A. Masood, H. Dawe: Responsible AI in the Enterprise https://www.oreilly.com/library/view/responsible-ai-in/9781803230528/
[ML software architecture] ML software architecture https://appliedaiinitiative.notion.site/ML-software-architecture-790b9f5fcfcf408884287acb82f4d75e
[Molnar 2024] C. Molnar: Interpretable Machine Learning, 2nd ed. https://christophm.github.io/interpretable-ml-book/

[Nahar et al.] N. Nahar, et al.: A meta-summary of challenges in building products with ml components–collecting experiences from 4758+ practitioners. 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN). IEEE, 2023.
[NirDiamant] RAG Techniques https://github.com/NirDiamant/RAG_Techniques
[Nist] NIST AI Risk Management Framework. https://www.nist.gov/itl/ai-risk-management-framework

[Osipov 2022] C. Osipov: MLOps Engineering at Scale https://www.manning.com/books/mlops-engineering-at-scale

[Parnin] Building Your Own Product Copilot: Challenges, Opportunities, and Needs https://arxiv.org/pdf/2312.14231v1
[Pruksachatkun et al. 2023] Y. Pruksachatkun, M. Mcateer, S. Majudmar: Practicing Trustworthy Machine Learning https://www.oreilly.com/library/view/practicing-trustworthy-machine/9781098120269/

[Reis et al.] J. Reis, M. Housley: Fundamentals of Data Engineering https://learning.oreilly.com/library/view/fundamentals-of-data/9781098108298/
[Roser 2022] Roser, Max: Brief History of AI: https://ourworldindata.org/brief-history-of-ai

[Salama et al.] K. Salama, J. Kazmierczak, D. Schut: Practitioners guide to MLOps: A framework for continuous delivery and automation of machine learning. https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf
[Saltz] J. Saltz: The GenAI Life Cycle https://www.datascience-pm.com/the-genai-life-cycle/
[Sanderson et al.] C. Sanderson, M. Freeman: Data Contracts https://learning.oreilly.com/library/view/data-contracts/9781098157623/
[Sarkis] A. Sarkis: Training Data for Machine Learning https://learning.oreilly.com/library/view/training-data-for/9781492094517/
[Savarese] S. Savarese: How AI Agents Will Revolutionize the AI Enterprise https://blog.salesforceairesearch.com/how-ai-agents-will-revolutionize-the-ai-enterprise/
[Serban] A. Serban, J. Visser: "Adapting software architectures to machine learning challenges." 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2022.
[Serra] J. Serra: Deciphering Data Architectures https://learning.oreilly.com/library/view/deciphering-data-architectures/9781098150754/
[Spirin et al.] N. Spirin, M. Balint: Mastering LLM Techniques: LLMOps https://developer.nvidia.com/blog/mastering-llm-techniques-llmops/
[Studer et al.] S. Studer et al.: Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology https://arxiv.org/abs/2003.05155

[Tan et al.] D. Tan, A. Leung, D. Colls: Effective Machine Learning Teams https://learning.oreilly.com/library/view/effective-machine-learning/9781098144623/
[Tan Wei Hao et al. 2024] B. Tan Wei Hao, S. Padmanabhan, V. Mallya: Design a Machine Learning System (From Scratch) https://www.manning.com/books/design-a-machine-learning-system-design-from-scratch
[tdcox] MLOps Roadmap 2024 - DRAFT https://github.com/cdfoundation/sig-mlops/blob/main/roadmap/2024/MLOpsRoadmap2024.md
[Treveil et al. 2020] M. Treveil, N. Omont, C. Stenac, K. Lefevre, D. Phan, J. Zentici, A. Lavoillotte, M. Miyazaki, L. Heidmann: Introducing MLOps https://www.oreilly.com/library/view/introducing-mlops/9781492083283/
[TU Berlin] Architecture of Machine Learning Systems (TU Berlin, SS 2024): https://mboehm7.github.io/teaching/ss24_amls/index.htm

[Vaughan 2020] Vaughan, D.: Analytical Skills for AI and Data Science (AI Use Cases) https://learning.oreilly.com/library/view/analytical-skills-for/9781492060932/
[Visengeriyeva, JTF] Visengeriyeva, L.: Defining Jagged Technological Frontier:https://www.perplexity.ai/page/defining-jagged-technological-iF8sDPVFQEKSdd2oyytztA
[Visengeriyeva, J-Curve] Visengeriyeva, L.: The Productivity J-Curve of AI: https://www.perplexity
[Visengeriyeva, AI Agents] Visengeriyeva, L.: AI Agents vs. Traditional Models https://www.perplexity.ai/page/ai-agents-vs-traditional-model-JFf4gKT0RySW_Ehvbxho2g
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[Wang et al. 2024] C. Wang et al.: Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices https://arxiv.org/pdf/2402.16391
[Wilson 2022] B. Wilson: Machine Learning Engineering in Action https://www.manning.com/books/machine-learning-engineering-in-action

[Zaharia et al.] M. Zaharia et al.: The Shift from Models to Compound AI Systems https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/