diff --git a/curriculum-swarc4ai-de.html b/curriculum-swarc4ai-de.html
index e782e8f..4cceb05 100644
--- a/curriculum-swarc4ai-de.html
+++ b/curriculum-swarc4ai-de.html
@@ -1897,8 +1897,8 @@
 <div id="header">
 <h1>Curriculum für<br><br>Certified Professional for<br>Software Architecture (CPSA)<sup>&#174;</sup><br><em>Advanced Level</em>: <br> Modul<br>SWARC4AI<br><br>Softwarearchitektur für KI-Systeme</h1>
 <div class="details">
-<span id="revnumber"> 2024.1-rev0-DE,</span>
-<span id="revdate">20241128</span>
+<span id="revnumber"> 2024.1-rev1-DE,</span>
+<span id="revdate">20250208</span>
 </div>
 <div id="toc" class="toc2">
 <div id="toctitle">Inhaltsverzeichnis</div>
@@ -2505,25 +2505,26 @@ <h3 id="_voraussetzungen">Voraussetzungen</h3>
 <p>Ein grundlegendes Verständnis von Künstlicher Intelligenz, Machine Learning und Data Science</p>
 </li>
 <li>
-<p>Vertrautheit mit Machine Learning-Methoden, wie Supervised Learning, Unsupervised Learning und Reinforcement Learning, einschließlich Klassifikation, Clustering, Regression und Dimensionality Reduction</p>
+<p>Ein grundlegendes Verständnis für Softwarearchitektur, DevOps und den Entwurf von Softwaresystemen sowie APIs</p>
 </li>
 <li>
-<p>Grundkenntnisse über gängige Algorithmen, wie z. B. lineare und logistische Regression, Entscheidungsbäume, Random Forests, Support Vector Machines, K-Means Clustering sowie neuronale Netze und Deep Learning</p>
+<p>Ein grundlegendes Wissen über die Programmiersprache Python und ihre Nutzung für KI-Probleme</p>
 </li>
 <li>
-<p>Ein Verständnis der Modellevaluation, einschließlich Cross-Validation, relevanter Metriken (Accuracy, Precision, Recall, F1-Score) und des Bias-Variance Tradeoffs</p>
-</li>
-<li>
-<p>Erfahrung mit typischen Frameworks wie scikit-learn, TensorFlow und PyTorch</p>
-</li>
-<li>
-<p>Vertrautheit mit Jupyter Notebooks oder ähnlichen Werkzeugen zur schnellen Anwendungsentwicklung (Rapid Application Development, RAD)</p>
+<p>Überblick über gängige Bibliotheken wie scikit-learn, TensorFlow und PyTorch</p>
 </li>
+</ul>
+</div>
+<div class="paragraph">
+<p>Wissen in folgenden Bereichen können <strong>hilfreich</strong> für das Verständnis einiger Konzepte sein:</p>
+</div>
+<div class="ulist">
+<ul>
 <li>
-<p>Ein grundlegendes Verständnis der technischen Verfahren hinter Künstlicher Intelligenz, einschließlich Maschinellen Lernens (insbesondere Deep Learning und LLMs) sowie Ansätzen im Bereich Natural Language Processing (NLP) und Transformationsmodellen</p>
+<p>Erfahrung mit der Kommandozeile auf Linux-Systemen</p>
 </li>
 <li>
-<p>Praktische Erfahrung im Training von Künstlichen Intelligenz-Modellen</p>
+<p>Wissen aus der iSAQB CPSA Foundation-Level Schulung für ein allgemeines Verständnis für Softwarearchitektur, Entwurfsmuster und Methodiken</p>
 </li>
 </ul>
 </div>
@@ -2700,8 +2701,8 @@ <h4 id="LZ-1-10">LZ 1-10: Konzept der Productivity J-Curve in Verbindung mit KI-
 <div class="sect2">
 <h3 id="_referenzen">1.3. Referenzen</h3>
 <div class="paragraph">
-<p><a href="#roser">[Roser 2022]</a>, <a href="#burkov">[Burkov 2019]</a>, <a href="#geron">[Géron 2022]</a>, <a href="#kelleher">[Kelleher 2015]</a>, <a href="#vaughan">[Vaughan 2020]</a>,
-<a href="#bahree">[Bahree 2024]</a>, <a href="#harvard">[Harvard et al. 2024]</a>, <a href="#dellacqua">[Dell&#8217;Acqua 2022]</a>, <a href="#visenger-one">[Visengeriyeva, JTF]</a>, <a href="#visenger-two">[Visengeriyeva, J-Curve]</a>,
+<p><a href="#roser">[Roser 2022]</a>, <a href="#brynjolfsson">[Brynjolfsson et al.]</a>, <a href="#burkov">[Burkov 2019]</a>, <a href="#geron">[Géron 2022]</a>, <a href="#kelleher">[Kelleher 2015]</a>, <a href="#vaughan">[Vaughan 2020]</a>,
+<a href="#bahree">[Bahree 2024]</a>, <a href="#harvard">[Harvard et al. 2024]</a>, <a href="#dellacqua">[Dell&#8217;Acqua 2022]</a>, <a href="#visenger-one">[Visengeriyeva, JTF]</a>,
 <a href="#agrawal">[Agrawal et al.]</a>, <a href="#tan">[Tan et al.]</a>, <a href="#chong">[Chong et al.]</a>, <a href="#hall">[Hall et al. 2023]</a>, <a href="#huyen">[Huyen 2022]</a>, <a href="#wang">[Wang et al. 2024]</a></p>
 </div>
 <div style="page-break-after: always;"></div>
@@ -3404,7 +3405,7 @@ <h4 id="LZ-5-10">LZ 5-10: Plattformen für die Modellbereitstellung kennen</h4>
 <div class="sect3">
 <h4 id="LZ-5-11">LZ 5-11: Werkzeuge für das Erstellen von POCs von KI-Systemen einordnen</h4>
 <div class="paragraph">
-<p>Die Teilnehmer:innen können gängige Werkzeuge für das Erstellen von POCs von KI-Systemen, wie beispielsweise Gradi, nennen und verstehen die konzeptionelle Funktionsweise.</p>
+<p>Die Teilnehmer:innen können gängige Werkzeuge für das Erstellen von POCs von KI-Systemen, wie beispielsweise Gradio, nennen und verstehen die konzeptionelle Funktionsweise.</p>
 </div>
 </div>
 <div class="sect3">
@@ -3881,6 +3882,9 @@ <h2 id="_referenzen_7">Referenzen</h2>
 <p><a id="bornstein-radovanic"></a>[bornstein-radovanic] M. Bornstein and R. Radovanovic: Emerging Architectures for LLM Applications <a href="https://a16z.com/emerging-architectures-for-llm-applications/" class="bare">https://a16z.com/emerging-architectures-for-llm-applications/</a></p>
 </li>
 <li>
+<p><a id="brynjolfsson"></a>[Brynjolfsson et al.] Brynjolfsson, E.: The Productivity J-Curve: How Intangibles complement General Purpose Technologies <a href="https://www.nber.org/system/files/working_papers/w25148/w25148.pdf" class="bare">https://www.nber.org/system/files/working_papers/w25148/w25148.pdf</a></p>
+</li>
+<li>
 <p><a id="burkov"></a>[Burkov 2019] Burkov, A.: The Hundred-Page Machine Learning Book <a href="https://themlbook.com/" class="bare">https://themlbook.com/</a></p>
 </li>
 </ul>
@@ -4168,9 +4172,6 @@ <h2 id="_referenzen_7">Referenzen</h2>
 <p><a id="visenger-one"></a>[Visengeriyeva, JTF] Visengeriyeva, L.: Defining Jagged Technological Frontier:https://www.perplexity.ai/page/defining-jagged-technological-iF8sDPVFQEKSdd2oyytztA</p>
 </li>
 <li>
-<p><a id="visenger-two"></a>[Visengeriyeva, J-Curve] Visengeriyeva, L.: The Productivity J-Curve of AI: <a href="https://www.perplexity" class="bare">https://www.perplexity</a></p>
-</li>
-<li>
 <p><a id="visenger-three"></a>[Visengeriyeva, AI Agents] Visengeriyeva, L.: AI Agents vs. Traditional Models <a href="https://www.perplexity.ai/page/ai-agents-vs-traditional-model-JFf4gKT0RySW_Ehvbxho2g" class="bare">https://www.perplexity.ai/page/ai-agents-vs-traditional-model-JFf4gKT0RySW_Ehvbxho2g</a></p>
 </li>
 <li>
@@ -4207,8 +4208,8 @@ <h2 id="_referenzen_7">Referenzen</h2>
 </div>
 <div id="footer">
 <div id="footer-text">
- 2024.1-rev0-DE<br>
-Last updated 2024-11-28 20:13:45 UTC
+ 2024.1-rev1-DE<br>
+Last updated 2025-02-08 19:11:46 UTC
 </div>
 </div>
 </body>
diff --git a/curriculum-swarc4ai-de.pdf b/curriculum-swarc4ai-de.pdf
index 73d075d..69276a0 100644
Binary files a/curriculum-swarc4ai-de.pdf and b/curriculum-swarc4ai-de.pdf differ
diff --git a/curriculum-swarc4ai-en.html b/curriculum-swarc4ai-en.html
index d85b943..d8ad128 100644
--- a/curriculum-swarc4ai-en.html
+++ b/curriculum-swarc4ai-en.html
@@ -1897,8 +1897,8 @@
 <div id="header">
 <h1>Curriculum for<br><br>Certified Professional for<br>Software Architecture (CPSA)<sup>&#174;</sup><br><em>Advanced Level</em>: <br> Module<br>SWARC4AI<br><br>Software Architecture for AI Systems</h1>
 <div class="details">
-<span id="revnumber"> 2024.1-rev0-EN,</span>
-<span id="revdate">20241128</span>
+<span id="revnumber"> 2024.1-rev1-EN,</span>
+<span id="revdate">20250208</span>
 </div>
 <div id="toc" class="toc2">
 <div id="toctitle">Table of Contents</div>
@@ -1907,7 +1907,7 @@ <h1>Curriculum for<br><br>Certified Professional for<br>Software Architecture (C
 <li><a href="#_introduction_general_information_about_the_isaqb_advanced_level">Introduction: General information about the iSAQB Advanced Level</a>
 <ul class="sectlevel2">
 <li><a href="#_what_is_taught_in_an_advanced_level_module">What is taught in an Advanced Level module?</a></li>
-<li><a href="#_what_can_advanced_level_cpsa_a_graduates_do">What can Advanced Level (CPSA-A) graduates do?</a></li>
+<li><a href="#_what_are_advanced_level_cpsa_a_graduates_qualified_for">What are Advanced Level (CPSA-A) graduates qualified for?</a></li>
 <li><a href="#_requirements_for_cpsa_a_certification">Requirements for CPSA-A certification</a></li>
 </ul>
 </li>
@@ -1915,58 +1915,57 @@ <h1>Curriculum for<br><br>Certified Professional for<br>Software Architecture (C
 <ul class="sectlevel2">
 <li><a href="#_what_does_the_module_swarc4ai_convey">What does the module “SWARC4AI” convey?</a></li>
 <li><a href="#_curriculum_structure_and_recommended_durations">Curriculum Structure and Recommended Durations</a></li>
-<li><a href="#_duration_teaching_method_and_further_details">Duration, Teaching Method and Further Details</a></li>
+<li><a href="#_duration_didactics_and_further_details">Duration, didactics and further details</a></li>
 <li><a href="#_prerequisites">Prerequisites</a></li>
-<li><a href="#_structure_of_the_curriculum">Structure of the Curriculum</a></li>
+<li><a href="#_structure_of_the_curriculum">Structure of the curriculum</a></li>
 <li><a href="#_supplementary_information_terms_translations">Supplementary Information, Terms, Translations</a></li>
 </ul>
 </li>
-<li><a href="#_this_is_the_first_modules_title">1. This is the First Module&#8217;s Title</a>
+<li><a href="#_introduction_to_software_architecture_relevant_concepts_for_artificial_intelligence">1. Introduction to software architecture-relevant concepts for artificial intelligence</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles">1.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts">1.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals">1.2. Learning Goals</a></li>
 <li><a href="#_references">1.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_heres_the_title_of_the_second_lesson">2. Here&#8217;s the Title of the Second Lesson</a>
+<li><a href="#_compliance_security_alignment">2. Compliance, security, alignment</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_2">2.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts_2">2.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals_2">2.2. Learning Goals</a></li>
 <li><a href="#_references_2">2.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_the_third_modules_title">3. The Third Module&#8217;s Title</a>
+<li><a href="#_design_and_development_of_ai_systems">3. Design and development of AI systems</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_3">3.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts_3">3.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals_3">3.2. Learning Goals</a></li>
 <li><a href="#_references_3">3.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_fourth_module_this_is_its_title">4. Fourth Module, This is its Title</a>
+<li><a href="#_data_management_and_data_processing_for_ai_systems">4. Data management and data processing for AI systems</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_4">4.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts_4">4.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals_4">4.2. Learning Goals</a></li>
 <li><a href="#_references_4">4.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_and_this_is_module_no_5">5. And This is Module no 5</a>
+<li><a href="#_important_quality_features_for_the_operation_of_ai_systems">5. Important quality features for the operation of AI systems</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_5">5.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts_5">5.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals_5">5.2. Learning Goals</a></li>
 <li><a href="#_references_5">5.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_and_this_is_module_no_6">6. And This is Module no 6</a>
+<li><a href="#_system_architectures_and_platforms_for_generative_ai_systems">6. System architectures and platforms for generative AI systems</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_6">6.1. Terms and Principles</a></li>
+<li><a href="#_terms_and_concepts_6">6.1. Terms and Concepts</a></li>
 <li><a href="#_learning_goals_6">6.2. Learning Goals</a></li>
 <li><a href="#_references_6">6.3. References</a></li>
 </ul>
 </li>
-<li><a href="#_and_this_is_module_no_7">7. And This is Module no 7</a>
+<li><a href="#_case_studies_and_practical_projects">7. Case studies and practical projects</a>
 <ul class="sectlevel2">
-<li><a href="#_terms_and_principles_7">7.1. Terms and Principles</a></li>
-<li><a href="#_learning_goals_7">7.2. Learning Goals</a></li>
+<li><a href="#_learning_goals_7">7.1. Learning Goals</a></li>
 </ul>
 </li>
 <li><a href="#_references_7">References</a></li>
@@ -2022,31 +2021,295 @@ <h2 id="_list_of_learning_goals">List of Learning Goals</h2>
 <div class="ulist">
 <ul>
 <li>
-<p><a href="#LG-1-1">LG 1-1: This the first learning goal, in category xy</a></p>
+<p><a href="#LG-1-1">LG 1-1: Classify artificial intelligence and machine learning, data science, deep learning, generative AI</a></p>
 </li>
 <li>
-<p><a href="#LG-2-1">LG 2-1: TBD</a></p>
+<p><a href="#LG-1-2">LG 1-2: Specify typical general use cases for AI</a></p>
 </li>
 <li>
-<p><a href="#LG-2-2">LG 2-2: TBD</a></p>
+<p><a href="#LG-1-3">LG 1-3: Know possible applications in different industries and end-user applications</a></p>
 </li>
 <li>
-<p><a href="#LG-3-1">LG 3-1: TBD</a></p>
+<p><a href="#LG-1-4">LG 1-4: Identify risks in the application of AI</a></p>
 </li>
 <li>
-<p><a href="#LG-3-2">LG 3-2: TBD</a></p>
+<p><a href="#LG-1-5">LG 1-5: Understanding differences to traditional software</a></p>
 </li>
 <li>
-<p><a href="#LG-4-1">LG 4-1: TBD</a></p>
+<p><a href="#LG-1-6">LG 1-6: Decide on solutions to problems using AI or classic software development</a></p>
 </li>
 <li>
-<p><a href="#LG-4-2">LG 4-2: TBD</a></p>
+<p><a href="#LG-1-7">LG 1-7: Know roles and their tasks as well as their cooperation in the context of AI</a></p>
 </li>
 <li>
-<p><a href="#LG-5-1">LG 5-1: TBD</a></p>
+<p><a href="#LG-1-8">LG 1-8: Identify and prioritize AI use cases</a></p>
 </li>
 <li>
-<p><a href="#LG-5-2">LG 5-2: TBD</a></p>
+<p><a href="#LG-1-9">LG 1-9: Know the strengths and limitations of AI</a></p>
+</li>
+<li>
+<p><a href="#LG-1-10">LG 1-10: Know the concept of the Productivity J-Curve in conjunction with AI technology</a></p>
+</li>
+<li>
+<p><a href="#LG-2-1">LG 2-1: Know the influence of data protection laws on the implementation and use of AI</a></p>
+</li>
+<li>
+<p><a href="#LG-2-2">LG 2-2: Understand the objectives and regulations of the EU AI Act and their impact on the development process and architecture</a></p>
+</li>
+<li>
+<p><a href="#LG-2-3">LG 2-3: Perform classification of AI systems according to the EU AI Act risk level</a></p>
+</li>
+<li>
+<p><a href="#LG-2-4">LG 2-4: Classify copyright issues of AI-generated content</a></p>
+</li>
+<li>
+<p><a href="#LG-2-5">LG 2-5: Overview of types and degrees of openness and types of licenses of free ML models</a></p>
+</li>
+<li>
+<p><a href="#LG-2-6">LG 2-6: Understand strategies for compliance with the EU AI Act and potential challenges</a></p>
+</li>
+<li>
+<p><a href="#LG-2-7">LG 2-7: Document models and data sets for traceability and transparency</a></p>
+</li>
+<li>
+<p><a href="#LG-2-8">LG 2-8: Know security pitfalls and types of attack on ML models</a></p>
+</li>
+<li>
+<p><a href="#LG-2-9">LG 2-9: Know and apply strategies for AI risk minimization</a></p>
+</li>
+<li>
+<p><a href="#LG-2-10">LG 2-10: Apply options for protection against attacks (AI security)</a></p>
+</li>
+<li>
+<p><a href="#LG-2-11">LG 2-11: Understanding the basic issues and facets of AI safety</a></p>
+</li>
+<li>
+<p><a href="#LG-2-12">LG 2-12: Understand ethical problems of AI systems and know approaches to dealing with them</a></p>
+</li>
+<li>
+<p><a href="#LG-2-13">LG 2-13: Overview of ethical guidelines</a></p>
+</li>
+<li>
+<p><a href="#LG-2-14">LG 2-14: Know the core principles of AI governance and responsible AI for companies</a></p>
+</li>
+<li>
+<p><a href="#LG-2-15">LG 2-15: Gain insight into the creation of regulatory sandboxes</a></p>
+</li>
+<li>
+<p><a href="#LG-2-16">LG 2-16: Ensuring effective data management for the quality and security of data in AI applications</a></p>
+</li>
+<li>
+<p><a href="#LG-3-1">LG 3-1: Understanding the life cycle of a machine learning or data science project</a></p>
+</li>
+<li>
+<p><a href="#LG-3-2">LG 3-2: Knowing process models for the software development of AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-3">LG 3-3: Know types of and requirements for data and typical ML problems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-4">LG 3-4: Understanding machine learning problems and their requirements</a></p>
+</li>
+<li>
+<p><a href="#LG-3-5">LG 3-5: Use input data for various AI algorithms</a></p>
+</li>
+<li>
+<p><a href="#LG-3-6">LG 3-6: Dealing with challenges such as non-determinism, data quality and concept and model drift</a></p>
+</li>
+<li>
+<p><a href="#LG-3-7">LG 3-7: Knowing transfer learning or fine-tuning</a></p>
+</li>
+<li>
+<p><a href="#LG-3-8">LG 3-8: Selecting design patterns for AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-9">LG 3-9: Define the task of an ML model</a></p>
+</li>
+<li>
+<p><a href="#LG-3-10">LG 3-10: Understand and specify inputs and outputs for the functioning of an ML system</a></p>
+</li>
+<li>
+<p><a href="#LG-3-11">LG 3-11: Know metrics for measuring the performance of ML models</a></p>
+</li>
+<li>
+<p><a href="#LG-3-12">LG 3-12: Integrating ML models into existing systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-13">LG 3-13: Designing user interfaces for AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-14">LG 3-14: Understanding performance metrics such as latency and throughput in AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-15">LG 3-15: Managing scalability for increased data volumes</a></p>
+</li>
+<li>
+<p><a href="#LG-3-16">LG 3-16: Understanding robustness in AI systems and applying strategies to increase robustness</a></p>
+</li>
+<li>
+<p><a href="#LG-3-17">LG 3-17: Classify the reliability and availability of AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-18">LG 3-18: Understanding the reproducibility and testability of AI results</a></p>
+</li>
+<li>
+<p><a href="#LG-3-19">LG 3-19: Know security, data protection and compliance requirements</a></p>
+</li>
+<li>
+<p><a href="#LG-3-20">LG 3-20: Classify explainability and interpretability in AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-3-21">LG 3-21: Recognizing bias in data and models</a></p>
+</li>
+<li>
+<p><a href="#LG-3-22">LG 3-22: Knowing fault tolerance in AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-4-1">LG 4-1: Acquiring and labeling data</a></p>
+</li>
+<li>
+<p><a href="#LG-4-2">LG 4-2: Know common platforms for publicly accessible data</a></p>
+</li>
+<li>
+<p><a href="#LG-4-3">LG 4-3: Overview of relevant tools for data labeling</a></p>
+</li>
+<li>
+<p><a href="#LG-4-4">LG 4-4: Designing efficient data pipelines and architectures</a></p>
+</li>
+<li>
+<p><a href="#LG-4-5">LG 4-5: Know strategies for data aggregation, cleansing, transformation, enrichment and augmentation</a></p>
+</li>
+<li>
+<p><a href="#LG-4-6">LG 4-6: Overview of tools for data engineering pipelines</a></p>
+</li>
+<li>
+<p><a href="#LG-4-7">LG 4-7: Know the options for storing data</a></p>
+</li>
+<li>
+<p><a href="#LG-5-1">LG 5-1: Know (hardware) requirements for training and inference</a></p>
+</li>
+<li>
+<p><a href="#LG-5-2">LG 5-2: Know the trade-offs of different model architectures with regard to quality characteristics</a></p>
+</li>
+<li>
+<p><a href="#LG-5-3">LG 5-3: Adjust different quality features of an ML model</a></p>
+</li>
+<li>
+<p><a href="#LG-5-4">LG 5-4: Understanding the costs, power consumption and sustainable use of AI (Green IT)</a></p>
+</li>
+<li>
+<p><a href="#LG-5-5">LG 5-5: Know (hardware) requirements for training and inference</a></p>
+</li>
+<li>
+<p><a href="#LG-5-6">LG 5-6: Model training, parameters, metrics and results tracking</a></p>
+</li>
+<li>
+<p><a href="#LG-5-7">LG 5-7: Evaluate ML models and AI systems based on them</a></p>
+</li>
+<li>
+<p><a href="#LG-5-8">LG 5-8: Know types of drift and possible causes and solutions for them</a></p>
+</li>
+<li>
+<p><a href="#LG-5-9">LG 5-9: Overview of CI/CD pipelines, model management and deployment strategies for AI models</a></p>
+</li>
+<li>
+<p><a href="#LG-5-10">LG 5-10: Know platforms for model provision</a></p>
+</li>
+<li>
+<p><a href="#LG-5-11">LG 5-11: Classify tools for the creation of POCs of AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-5-12">LG 5-12: Knowing the deployment options of AI models</a></p>
+</li>
+<li>
+<p><a href="#LG-5-13">LG 5-13: List advantages and disadvantages of SaaS and self-hosting</a></p>
+</li>
+<li>
+<p><a href="#LG-5-14">LG 5-14: Overview of SaaS AI solutions</a></p>
+</li>
+<li>
+<p><a href="#LG-5-15">LG 5-15: Know embedded deployments of ML models</a></p>
+</li>
+<li>
+<p><a href="#LG-5-16">LG 5-16: Understanding monitoring with regard to AI-specific requirements</a></p>
+</li>
+<li>
+<p><a href="#LG-5-17">LG 5-17: Overview of sample tools for monitoring</a></p>
+</li>
+<li>
+<p><a href="#LG-5-18">LG 5-18: Understanding user feedback and methods and tools for collecting user feedback</a></p>
+</li>
+<li>
+<p><a href="#LG-5-19">LG 5-19: Know methods for using feedback for model training</a></p>
+</li>
+<li>
+<p><a href="#LG-5-20">LG 5-20: [OPTIONAL] Understanding the MLOps pipeline using a practical example</a></p>
+</li>
+<li>
+<p><a href="#LG-5-21">LG 5-21: [OPTIONAL] Make build vs. buy decisions for MLOps systems/components</a></p>
+</li>
+<li>
+<p><a href="#LG-5-22">LG 5-22: [OPTIONAL] Know MLOps tools and end-to-end platforms</a></p>
+</li>
+<li>
+<p><a href="#LG-6-1">LG 6-1: Overview of integration levels of AI</a></p>
+</li>
+<li>
+<p><a href="#LG-6-2">LG 6-2: Know libraries, interfaces and tools for the integration of AI models</a></p>
+</li>
+<li>
+<p><a href="#LG-6-3">LG 6-3: Integrate AI systems into the overall architecture of an IT landscape</a></p>
+</li>
+<li>
+<p><a href="#LG-6-4">LG 6-4: Overview of relevant quality features for AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-6-5">LG 6-5: [OPTIONAL] Know evaluation frameworks for AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-6-6">LG 6-6: [OPTIONAL] Discuss a case study with an imaginary professionalism</a></p>
+</li>
+<li>
+<p><a href="#LG-6-7">LG 6-7: Fundamental understanding of generative AI</a></p>
+</li>
+<li>
+<p><a href="#LG-6-8">LG 6-8: Understanding how LLMs work</a></p>
+</li>
+<li>
+<p><a href="#LG-6-9">LG 6-9: Understand known patterns in the use of LLMs</a></p>
+</li>
+<li>
+<p><a href="#LG-6-10">LG 6-10: Knowing use cases for RAG (Retrieval-Augmented Generation)</a></p>
+</li>
+<li>
+<p><a href="#LG-6-11">LG 6-11: Knowing and understanding selected RAG techniques</a></p>
+</li>
+<li>
+<p><a href="#LG-6-12">LG 6-12: Know types of prompt engineering</a></p>
+</li>
+<li>
+<p><a href="#LG-6-13">LG 6-13: Overview of agentic workflows</a></p>
+</li>
+<li>
+<p><a href="#LG-6-14">LG 6-14: Know a selection of design patterns for generative AI systems</a></p>
+</li>
+<li>
+<p><a href="#LG-6-15">LG 6-15: Know techniques for evaluating LLM applications</a></p>
+</li>
+<li>
+<p><a href="#LG-6-16">LG 6-16: Overview of known LLMs and selection criteria</a></p>
+</li>
+<li>
+<p><a href="#LG-6-17">LG 6-17: Understanding the importance of cost management for GenAI applications</a></p>
+</li>
+<li>
+<p><a href="#LG-6-18">LG 6-18: Give examples of common libraries, interfaces and tools related to LLM applications</a></p>
+</li>
+<li>
+<p><a href="#LG-6-19">LG 6-19: [OPTIONAL] Know agentic AI software architectures, AI agent architecture components and types of AI agent architectures</a></p>
+</li>
+<li>
+<p><a href="#LG-7-1">LG 7-1: [OPTIONAL] Apply the acquired knowledge in real-life scenarios using case studies and practical projects</a></p>
 </li>
 </ul>
 </div>
@@ -2058,35 +2321,38 @@ <h2 id="_introduction_general_information_about_the_isaqb_advanced_level">Introd
 <div class="sectionbody">
 <div class="sect2">
 <h3 id="_what_is_taught_in_an_advanced_level_module">What is taught in an Advanced Level module?</h3>
+<div class="paragraph">
+<p>The module can be attended independently of a CPSA-F certification.</p>
+</div>
 <div class="ulist">
 <ul>
 <li>
-<p>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.</p>
+<p>The iSAQB Advanced Level offers modular training in three areas of competence with flexible training paths. It takes into account individual inclinations and specializations.</p>
 </li>
 <li>
-<p>The certification is done as an assignment. The assessment and oral exam is conducted by experts appointed by the iSAQB.</p>
+<p>The certification takes the form of a term paper. The assessment and oral examination are carried out by experts appointed by the iSAQB.</p>
 </li>
 </ul>
 </div>
 </div>
 <div class="sect2">
-<h3 id="_what_can_advanced_level_cpsa_a_graduates_do">What can Advanced Level (CPSA-A) graduates do?</h3>
+<h3 id="_what_are_advanced_level_cpsa_a_graduates_qualified_for">What are Advanced Level (CPSA-A) graduates qualified for?</h3>
 <div class="paragraph">
 <p>CPSA-A graduates can:</p>
 </div>
 <div class="ulist">
 <ul>
 <li>
-<p>Independently and methodically design medium to large IT systems</p>
+<p>design medium-sized to large IT systems independently and methodically</p>
 </li>
 <li>
-<p>In IT systems of medium to high criticality, assume technical and content-related responsibility</p>
+<p>assume technical and content-related responsibility in IT systems of medium to high criticality</p>
 </li>
 <li>
-<p>Conceptualize, design, and document actions to achieve quality requirements and support development teams in the implementation of these actions</p>
+<p>Conceptualize, design and document measures to achieve quality requirements and support development teams in the implementation of these measures</p>
 </li>
 <li>
-<p>Control and execute architecture-relevant communication in medium to large development teams</p>
+<p>Manage and carry out architecture-related communication in medium to large development teams</p>
 </li>
 </ul>
 </div>
@@ -2133,8 +2399,7 @@ <h2 id="_essentials">Essentials</h2>
 <div class="sect2">
 <h3 id="_what_does_the_module_swarc4ai_convey">What does the module “SWARC4AI” convey?</h3>
 <div class="paragraph">
-<p>The module presents SWARC4AI to the participants …
-At the end of the module, the participants know … and are able to …</p>
+<p>The module presents participants with modern software architecture concepts for AI systems as a means of designing powerful, scalable and integrable AI solutions. At the end of the module, participants will be familiar with the key principles of software architecture for AI systems and will be able to apply them to the design and implementation of machine learning and generative AI systems. With the help of the modeling techniques and architecture tools taught, they will be able to seamlessly integrate AI components into existing software systems. The course covers both machine learning systems and generative AI and teaches how these can be combined with classic software systems. Participants learn what the architecture for such hybrid systems must look like in order to ensure scalability, maintainability and extensibility.</p>
 </div>
 </div>
 <div class="sect2">
@@ -2146,56 +2411,50 @@ <h3 id="_curriculum_structure_and_recommended_durations">Curriculum Structure an
 </colgroup>
 <thead>
 <tr>
-<th class="tableblock halign-left valign-top">Content</th>
-<th class="tableblock halign-right valign-top">Recommended minimum duration (minutes)</th>
+<th class="tableblock halign-left valign-top">Contents</th>
+<th class="tableblock halign-right valign-top">Recommended minimum duration (min)</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">1. Introduction</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">180</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Introduction to concepts relevant to software architecture for artificial intelligence</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">120</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">2. xz</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">150</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Compliance, security, alignment</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">120</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">3. Lots of theory</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">120</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Design and development of AI systems</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">320</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">4. xy and example</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">180</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Data management and data processing for AI systems</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">90</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">5. abc und d</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">210</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Important quality features for the operation of AI systems</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">160</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">6. Final example</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">120</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">System architectures and platforms for generative AI systems</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">160</p></td>
 </tr>
 <tr>
-<td class="tableblock halign-left valign-top"></td>
-<td class="tableblock halign-right valign-top"></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Case studies and practical projects</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">110</p></td>
 </tr>
 <tr>
 <td class="tableblock halign-left valign-top"><p class="tableblock">Total</p></td>
-<td class="tableblock halign-right valign-top"><p class="tableblock">960 (16h)</p></td>
+<td class="tableblock halign-right valign-top"><p class="tableblock">1080</p></td>
 </tr>
 </tbody>
 </table>
 </div>
 <div class="sect2">
-<h3 id="_duration_teaching_method_and_further_details">Duration, Teaching Method and Further Details</h3>
-<div class="paragraph">
-<p>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.</p>
-</div>
+<h3 id="_duration_didactics_and_further_details">Duration, didactics and further details</h3>
 <div class="paragraph">
-<p>Licensed training courses for the SWARC4AI module contribute the following credit points towards admission to the final Advanced Level certification exam:</p>
+<p>The duration of a training course on the SWARC4AI module should be at least 3 days, but can be longer. Providers may differ in terms of duration, didactics, type and structure of the exercises and the detailed course structure. In particular, syllabus leaves the type of examples and exercises completely open. Licensed training courses on SWARC4AI contribute to admission to the final Advanced Level certification examination (following credit points):</p>
 </div>
 <table class="tableblock frame-none grid-rows stripes-none stretch">
 <colgroup>
@@ -2222,81 +2481,1016 @@ <h3 id="_duration_teaching_method_and_further_details">Duration, Teaching Method
 <div class="sect2">
 <h3 id="_prerequisites">Prerequisites</h3>
 <div class="paragraph">
-<p>Participants <strong>should</strong> have the following prerequisite knowledge:</p>
+<p>Participants should have the following knowledge and/or experience:</p>
 </div>
 <div class="ulist">
 <ul>
 <li>
-<p>Prerequisite 1</p>
+<p>A basic understanding of artificial intelligence, machine learning and data science</p>
 </li>
 <li>
-<p>Prerequisite 2, etc.</p>
+<p>A basic understanding of software architecture, DevOps and the design of software systems and APIs</p>
+</li>
+<li>
+<p>A basic knowledge of the Python programming language and its use for AI problems</p>
+</li>
+<li>
+<p>Overview of common libraries such as scikit-learn, TensorFlow and PyTorch</p>
 </li>
 </ul>
 </div>
 <div class="paragraph">
-<p>Knowledge in the following areas may be <strong>helpful</strong> for understanding some concepts:</p>
+<p>Knowledge in the following areas can be helpful for understanding some concepts</p>
 </div>
 <div class="ulist">
 <ul>
 <li>
-<p>Area 1:</p>
+<p>Experience with the command line interface on Linux systems</p>
+</li>
+<li>
+<p>Knowledge from the iSAQB CPSA Foundation level training for a general understanding of software architecture, design patterns and methodologies</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_structure_of_the_curriculum">Structure of the curriculum</h3>
+<div class="paragraph">
+<p>The individual sections of the curriculum are described according to the following structure:</p>
+</div>
 <div class="ulist">
 <ul>
 <li>
-<p>Knowledge 1</p>
+<p><strong>Terms/concepts</strong>: Essential core terms of this topic.</p>
+</li>
+<li>
+<p><strong>Teaching/practice time</strong>: Defines the minimum amount of teaching and practice time that must be spent on this topic or its practice in an accredited training course.</p>
 </li>
 <li>
-<p>Experience 2</p>
+<p><strong>Learning goals</strong>: Describes the content to be conveyed including its core terms and principles.</p>
 </li>
+</ul>
+</div>
+<div class="paragraph">
+<p>This section therefore also outlines the skills to be acquired in corresponding training courses.</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_supplementary_information_terms_translations">Supplementary Information, Terms, Translations</h3>
+<div class="paragraph">
+<p>To the extent necessary for understanding the curriculum, we have added definitions of technical terms to the <a href="https://github.com/isaqb-org/glossary">iSAQB glossary</a> and complemented them by adding referenes to the literature.</p>
+</div>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_introduction_to_software_architecture_relevant_concepts_for_artificial_intelligence">1. Introduction to software architecture-relevant concepts for artificial intelligence</h2>
+<div class="sectionbody">
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 50%;">
+<col style="width: 50%;">
+</colgroup>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 120 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 0 min</p></td>
+</tr>
+</tbody>
+</table>
+<div class="sect2">
+<h3 id="_terms_and_concepts">1.1. Terms and Concepts</h3>
+<div class="paragraph">
+<p>AI, Generative AI, Machine Learning, Symbolic AI, Evolutionary Algorithms, Statistical Learning, Deep Learning, LLMs, Hallucination, Bias, Jagged Technological Frontier, Data Preparation, Model Training, Model Evaluation, Feature Engineering, Data Science, Data Engineering</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_learning_goals">1.2. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-1-1">LG 1-1: Classify artificial intelligence and machine learning, data science, deep learning, generative AI</h4>
+<div class="paragraph">
+<p>Participants will know how artificial intelligence is defined and how machine learning, data science, deep learning and generative AI are categorized within it.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-2">LG 1-2: Specify typical general use cases for AI</h4>
+<div class="paragraph">
+<p>Participants will know how to specify typical general use cases for AI. This includes use cases for e.g. image recognition &amp; generation, language processing, prediction, personalization and anomaly detection.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-3">LG 1-3: Know possible applications in different industries and end-user applications</h4>
+<div class="paragraph">
+<p>Participants will be familiar with the possible applications in various industries, e.g. marketing, medicine, robotics and content creation. They will also gain an overview of the possible uses of AI in end-user applications. These include, for example, voice assistants (chatbots) and recommendation systems (recommender engines).</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-4">LG 1-4: Identify risks in the application of AI</h4>
+<div class="paragraph">
+<p>Participants can identify risks that arise when using AI. These can be the following risks, for example:</p>
+</div>
+<div class="ulist">
+<ul>
 <li>
-<p>Knowledge 3</p>
+<p>Hallucinations</p>
 </li>
 <li>
-<p>Experience 4</p>
+<p>Bias</p>
 </li>
 <li>
-<p>Understanding 5</p>
+<p>(un)fairness</p>
 </li>
-</ul>
-</div>
+<li>
+<p>societal risks such as deepfakes, AI-enabled cyberattacks, safety risks in critical systems, social manipulation, intellectual property issues, etc.</p>
 </li>
 </ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_structure_of_the_curriculum">Structure of the Curriculum</h3>
+<div class="sect3">
+<h4 id="LG-1-5">LG 1-5: Understanding differences to traditional software</h4>
 <div class="paragraph">
-<p>The individual sections of the curriculum are described according to the following structure:</p>
+<p>Participants understand the differences between AI systems and traditional software:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Data-driven (with ML) - data-centric instead of code-centric development</p>
+</li>
+<li>
+<p>Probabilistic results (non-deterministic behavior)</p>
+</li>
+<li>
+<p>Statistical validation</p>
+</li>
+<li>
+<p>Experimental design: Support for rapid iteration and testing of models.</p>
+</li>
+<li>
+<p>Model complexity and interpretability: ML models, especially deep learning models, can be extremely complex. The "black box" character makes them difficult to interpret and explain.</p>
+</li>
+<li>
+<p>Debugging and testing are more complex due to non-determinism.</p>
+</li>
+<li>
+<p>Model decay: Continuous monitoring and retraining are necessary to maintain model performance.</p>
+</li>
+<li>
+<p>AI-specific regulation of industries and at EU level must be taken into account.</p>
+</li>
+<li>
+<p>Interoperability: Seamless integration into existing systems and technology stacks.</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-6">LG 1-6: Decide on solutions to problems using AI or classic software development</h4>
+<div class="paragraph">
+<p>Participants can decide and explain whether and why a problem should be solved using either AI or classic software development.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-7">LG 1-7: Know roles and their tasks as well as their cooperation in the context of AI</h4>
+<div class="paragraph">
+<p>The participants know typical roles and their tasks in these contexts. The roles include in particular</p>
+</div>
+<div class="paragraph">
+<p>Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer, MLOps Engineer, AI Architect, Data Architect, Business Intelligence (BI) Developer, Data Governance Specialist and ML Researcher.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-8">LG 1-8: Identify and prioritize AI use cases</h4>
+<div class="paragraph">
+<p>Participants will be able to identify and prioritize AI use cases.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-9">LG 1-9: Know the strengths and limitations of AI</h4>
+<div class="paragraph">
+<p>Participants understand the strengths and limitations of AI and are familiar with the so-called "Jagged Technological Frontier".</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-1-10">LG 1-10: Know the concept of the Productivity J-Curve in conjunction with AI technology</h4>
+<div class="paragraph">
+<p>This phenomenon helps to understand why companies can initially experience a drop in productivity when implementing AI, but this can be followed by productivity gains as it develops further.</p>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_references">1.3. References</h3>
+<div class="paragraph">
+<p><a href="#roser">[Roser 2022]</a>, <a href="#brynjolfsson">[Brynjolfsson et al.]</a>, <a href="#burkov">[Burkov 2019]</a>, <a href="#geron">[Géron 2022]</a>, <a href="#kelleher">[Kelleher 2015]</a>, <a href="#vaughan">[Vaughan 2020]</a>,
+<a href="#bahree">[Bahree 2024]</a>, <a href="#harvard">[Harvard et al. 2024]</a>, <a href="#dellacqua">[Dell&#8217;Acqua 2022]</a>, <a href="#visenger-one">[Visengeriyeva, JTF]</a>,
+<a href="#agrawal">[Agrawal et al.]</a>, <a href="#tan">[Tan et al.]</a>, <a href="#chong">[Chong et al.]</a>, <a href="#hall">[Hall et al. 2023]</a>, <a href="#huyen">[Huyen 2022]</a>, <a href="#wang">[Wang et al. 2024]</a></p>
+</div>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_compliance_security_alignment">2. Compliance, security, alignment</h2>
+<div class="sectionbody">
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 50%;">
+<col style="width: 50%;">
+</colgroup>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 120 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 30 min</p></td>
+</tr>
+</tbody>
+</table>
+<div class="sect2">
+<h3 id="_terms_and_concepts_2">2.1. Terms and Concepts</h3>
+<div class="paragraph">
+<p>EU AI Act, data protection, copyright, license, open (source), AI security, jailbreak, adversarial attack, data poisoning, model inversion &amp; extraction, AI safety, AI ethics, AI alignment, model and data documentation, transparency obligation, human oversight, data governance, AI governance, AI systems by risk levels (prohibited, high-risk, limited risk, low-risk)</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_learning_goals_2">2.2. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-2-1">LG 2-1: Know the influence of data protection laws on the implementation and use of AI</h4>
+<div class="paragraph">
+<p>Participants are familiar with data protection laws such as the GDPR and know how they affect the collection, processing and storage of data by AI systems.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-2">LG 2-2: Understand the objectives and regulations of the EU AI Act and their impact on the development process and architecture</h4>
+<div class="paragraph">
+<p>The participants understand the objectives and regulations of the EU AI Act and know what influence this has on the development and use of AI systems. They also understand the requirements of the EU AI Act (Trustworthy AI) and what influence these requirements have on the development process and the architecture of the software system. In particular, they know the influence on some of the following aspects:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Risk management system (risk minimization)</p>
+</li>
+<li>
+<p>Data quality and data governance (quality management system)</p>
+</li>
+<li>
+<p>Creation and maintenance of comprehensive technical documentation for the AI system</p>
+</li>
+<li>
+<p>Automatic recording/logging of events in the AI system.</p>
+</li>
+<li>
+<p>Provision of clear and understandable information for users</p>
+</li>
+<li>
+<p>Implementation of measures for human supervision</p>
+</li>
+<li>
+<p>Ensuring an appropriate level of accuracy and robustness</p>
+</li>
+<li>
+<p>Implementation of cyber security measures</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-3">LG 2-3: Perform classification of AI systems according to the EU AI Act risk level</h4>
+<div class="paragraph">
+<p>The participants know the classification of AI systems according to the EU AI Act risk levels (prohibited, high-risk, limited-risk, low-risk) and know which regulatory requirements apply in each case.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-4">LG 2-4: Classify copyright issues of AI-generated content</h4>
+<div class="paragraph">
+<p>The participants understand the copyright issues for AI-generated content, know the effects on certain software license models and can devise potential solutions for these issues.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-5">LG 2-5: Overview of types and degrees of openness and types of licenses of free ML models</h4>
+<div class="paragraph">
+<p>The participants gain an overview of different types and degrees of openness of free ML models. This concerns, for example, the disclosure of data and model parameters. In addition, they know different types of licenses of free ML models and their impact on the AI system.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-6">LG 2-6: Understand strategies for compliance with the EU AI Act and potential challenges</h4>
+<div class="paragraph">
+<p>The participants understand the basic statements of the EU AI Act (in particular transparency obligations) and know strategies for compliance and possible challenges.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-7">LG 2-7: Document models and data sets for traceability and transparency</h4>
+<div class="paragraph">
+<p>Participants know how to effectively document models and data sets to ensure traceability and transparency.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-8">LG 2-8: Know security pitfalls and types of attack on ML models</h4>
+<div class="paragraph">
+<p>Participants will be familiar with possible security pitfalls and typical types of attack on ML models. This applies, for example:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>LLM jailbreaks through prompt engineering</p>
+</li>
+<li>
+<p>Adversarial Attacks</p>
+</li>
+<li>
+<p>Data Poisoning</p>
+</li>
+<li>
+<p>Model Inversion &amp; Extraction.</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-9">LG 2-9: Know and apply strategies for AI risk minimization</h4>
+<div class="paragraph">
+<p>Participants know how to develop and apply strategies to minimize AI risks. In particular, the participants know the following strategies:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Strengthening robustness through extensive testing</p>
+</li>
+<li>
+<p>Fault-tolerant AI systems</p>
+</li>
+<li>
+<p>Transparent development</p>
+</li>
+<li>
+<p>Explainable AI</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-10">LG 2-10: Apply options for protection against attacks (AI security)</h4>
+<div class="paragraph">
+<p>Participants will be familiar with various options for protecting against attacks (AI security) and, in particular, they will be familiar with options for integrating security standards into the architecture and can take these into account for the design of the system.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-11">LG 2-11: Understanding the basic issues and facets of AI safety</h4>
+<div class="paragraph">
+<p>The participants understand the basic problems of AI safety and know the various facets of it. In particular, participants will be familiar with specific problems such as "AI model risks by poisoning" and "bias".</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-12">LG 2-12: Understand ethical problems of AI systems and know approaches to dealing with them</h4>
+<div class="paragraph">
+<p>Participants are aware of the ethical problems that AI systems can bring with them and know approaches and possibilities for resolving these problems. This includes, for example, AI alignment (and its limits) and the creation of their own AI guidelines.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-13">LG 2-13: Overview of ethical guidelines</h4>
+<div class="paragraph">
+<p>The participants review important ethics guidelines such as the "EU Ethics Guidelines for trustworthy AI" and the "Google AI Ethics Guidelines".</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-14">LG 2-14: Know the core principles of AI governance and responsible AI for companies</h4>
+<div class="paragraph">
+<p>The participants are familiar with the most important documents on AI governance in order to develop the core principles of AI governance and responsible AI for the company. This applies in particular to the following documents:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>OECD AI Principles, <a href="https://oecd.ai/en/ai-principles" class="bare">https://oecd.ai/en/ai-principles</a></p>
+</li>
+<li>
+<p>The Asilomar AI Principles, <a href="https://futureoflife.org/open-letter/ai-principles/" class="bare">https://futureoflife.org/open-letter/ai-principles/</a></p>
+</li>
+<li>
+<p>The IEEE Ethically Aligned Design framework, <a href="https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf" class="bare">https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf</a></p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-15">LG 2-15: Gain insight into the creation of regulatory sandboxes</h4>
+<div class="paragraph">
+<p>Participants will gain an insight into the creation of regulatory sandboxes to promote innovation and the possible legal consequences of non-compliance with the provisions of the AI Act.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-2-16">LG 2-16: Ensuring effective data management for the quality and security of data in AI applications</h4>
+<div class="paragraph">
+<p>Participants will know how effective data management ensures the quality and security of data in AI applications.</p>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_references_2">2.3. References</h3>
+<div class="paragraph">
+<p><a href="#engler">[Engler et al.]</a>, <a href="#nist">[Nist]</a>, <a href="#hall">[Hall et al. 2023]</a>, <a href="#masood">[Masood et al. 2023]</a>, <a href="#csiro">[CSIRO et al. 2023]</a>,
+<a href="#pruksachatkun">[Pruksachatkun et al. 2023]</a>, <a href="#chen">[Chen et al. 2022]</a>, <a href="#atlas">[ATLAS]</a>, <a href="#visenger-links">[Visengeriyeva, Ethics]</a>,
+<a href="#eu-ai-act">[EU AI Act]</a>, <a href="#hotz">[Hotz, Best Practices]</a>, <a href="#bhajaria">[Bhajaria 2022]</a>, <a href="#jarmul">[Jarmul 2023]</a>, <a href="#molnar">[Molnar 2024]</a></p>
+</div>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_design_and_development_of_ai_systems">3. Design and development of AI systems</h2>
+<div class="sectionbody">
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 50%;">
+<col style="width: 50%;">
+</colgroup>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 320 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 45 min</p></td>
+</tr>
+</tbody>
+</table>
+<div class="sect2">
+<h3 id="_terms_and_concepts_3">3.1. Terms and Concepts</h3>
+<div class="paragraph">
+<p>CI/CD, MLOps, CRISP-ML(Q), AI application, AI engineering, ML model development, ML infrastructure, performance, robustness, reliability, fault tolerance, model-as-service, model-as-dependency, precompute, model-on-demand, hybrid serving, multi-agent system (MAS)</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_learning_goals_3">3.2. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-3-1">LG 3-1: Understanding the life cycle of a machine learning or data science project</h4>
+<div class="paragraph">
+<p>Participants have an understanding of the life cycle of a machine learning or data science project. In particular, they are familiar with the following phases:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Exploratory Data Analysis</p>
+</li>
+<li>
+<p>Data cleansing and preparation</p>
+</li>
+<li>
+<p>Feature Engineering</p>
+</li>
+<li>
+<p>Model training and selection</p>
+</li>
+<li>
+<p>POC</p>
+</li>
+<li>
+<p>Deployment</p>
+</li>
+<li>
+<p>Maintenance</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-2">LG 3-2: Knowing process models for the software development of AI systems</h4>
+<div class="paragraph">
+<p>Participants will be familiar with typical process models for the software development of AI systems. These are, for example</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>CRISP-ML(Q)</p>
+</li>
+<li>
+<p>Team Data Science Process</p>
+</li>
+<li>
+<p>GenAI Life Cycle.</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-3">LG 3-3: Know types of and requirements for data and typical ML problems</h4>
+<div class="paragraph">
+<p>Participants know different types of data and typical ML problems as well as use cases for utilizing the data. In addition, the participants have an understanding of various requirements for the data, e.g. the presence of correct labels of various types.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-4">LG 3-4: Understanding machine learning problems and their requirements</h4>
+<div class="paragraph">
+<p>The participants understand the various machine learning problems such as</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Supervised Learning</p>
+</li>
+<li>
+<p>Unsupervised Learning</p>
+</li>
+<li>
+<p>Reinforcement Learning</p>
+</li>
+</ul>
+</div>
+<div class="paragraph">
+<p>and know what requirements they have.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-5">LG 3-5: Use input data for various AI algorithms</h4>
+<div class="paragraph">
+<p>Participants know which data is required for AI algorithms and have a good understanding of the need for validation and knowledge of the typical data breakdown into training, validation and test data. In addition, participants have a good understanding of the input data required for various AI algorithms such as</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Neural networks as numerical vectors and matrices or tensors</p>
+</li>
+<li>
+<p>One-Hot-Encodings</p>
+</li>
+<li>
+<p>embeddings</p>
+</li>
+</ul>
+</div>
+<div class="paragraph">
+<p>are required.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-6">LG 3-6: Dealing with challenges such as non-determinism, data quality and concept and model drift</h4>
+<div class="paragraph">
+<p>Participants understand how to deal with challenges such as non-determinism, data quality and concept and model drift.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-7">LG 3-7: Knowing transfer learning or fine-tuning</h4>
+<div class="paragraph">
+<p>The participants are familiar with transfer learning and fine-tuning as a way of incorporating the pre-trained basic models into existing use cases.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-8">LG 3-8: Selecting design patterns for AI systems</h4>
+<div class="paragraph">
+<p>Participants will know which design patterns exist for AI systems and how to select suitable patterns. This concerns the following design patterns:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>ML Systems Topology Patterns</p>
+</li>
+<li>
+<p>Pipeline Architecture Patterns</p>
+</li>
+<li>
+<p>Model Training Pattern</p>
+</li>
+<li>
+<p>Model Serving Patterns</p>
+</li>
+<li>
+<p>Model Deployment Patterns</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-9">LG 3-9: Define the task of an ML model</h4>
+<div class="paragraph">
+<p>The participants know how to define the use cases / tasks of an ML model, such as the classification of images or fraud detection.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-10">LG 3-10: Understand and specify inputs and outputs for the functioning of an ML system</h4>
+<div class="paragraph">
+<p>Participants understand which inputs and outputs are required for the functioning of an ML system and can specify them.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-11">LG 3-11: Know metrics for measuring the performance of ML models</h4>
+<div class="paragraph">
+<p>Participants are familiar with various metrics for measuring the performance of ML models, such as:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Precision, Recall</p>
+</li>
+<li>
+<p>F1</p>
+</li>
+<li>
+<p>Accuracy</p>
+</li>
+</ul>
+</div>
+<div class="paragraph">
+<p>and know how to define evaluation criteria for performance assessment.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-12">LG 3-12: Integrating ML models into existing systems</h4>
+<div class="paragraph">
+<p>Participants understand how ML models can be integrated into existing systems and are familiar with the interfaces and integration points.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-13">LG 3-13: Designing user interfaces for AI systems</h4>
+<div class="paragraph">
+<p>Participants know how user interfaces should be designed to enable effective interactions with the ML system and optimize the user experience.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-14">LG 3-14: Understanding performance metrics such as latency and throughput in AI systems</h4>
+<div class="paragraph">
+<p>Participants understand the importance of key performance indicators such as latency and throughput in AI systems and know how these can be optimized.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-15">LG 3-15: Managing scalability for increased data volumes</h4>
+<div class="paragraph">
+<p>Participants understand the importance of scalability for increased data volumes and know how to develop AI systems that can handle increasing data volumes without losing performance.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-16">LG 3-16: Understanding robustness in AI systems and applying strategies to increase robustness</h4>
+<div class="paragraph">
+<p>Participants have an understanding of what robustness means in AI systems and can apply strategies to increase robustness in different application contexts.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-17">LG 3-17: Classify the reliability and availability of AI systems</h4>
+<div class="paragraph">
+<p>Participants understand the concepts of reliability and availability and know how to build AI systems that are stable and constantly available.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-18">LG 3-18: Understanding the reproducibility and testability of AI results</h4>
+<div class="paragraph">
+<p>The participants know how important it is that AI results are reproducible and testable and know which methods can be used to ensure these properties.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-19">LG 3-19: Know security, data protection and compliance requirements</h4>
+<div class="paragraph">
+<p>Participants are familiar with the requirements for security, data protection and compliance and know how these are implemented in AI systems.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-20">LG 3-20: Classify explainability and interpretability in AI systems</h4>
+<div class="paragraph">
+<p>Participants understand the importance of explainability and interpretability in AI systems and know how to ensure this in order to promote trust and transparency.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-21">LG 3-21: Recognizing bias in data and models</h4>
+<div class="paragraph">
+<p>The participants know how bias in data and models can be recognized and reduced in order to ensure fairness and equal treatment in AI applications.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-3-22">LG 3-22: Knowing fault tolerance in AI systems</h4>
+<div class="paragraph">
+<p>Participants are familiar with the concepts of fault tolerance and can explain how AI systems remain functional despite errors or malfunctions.</p>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_references_3">3.3. References</h3>
+<div class="paragraph">
+<p><a href="#tuberlin">[TU Berlin]</a>, <a href="#bornstein">[Bornstein et al.]</a>,
+<a href="#crowe">[Crowe et al. 2024]</a>, <a href="#lakshmanan">[Lakshmanan et al.]</a>,
+<a href="#alake">[Alake]</a>, <a href="#koc">[Koc]</a>,
+<a href="#cdteliot">[Cdteliot]</a>, <a href="#visenger-three">[Visengeriyeva, AI Agents]</a>,
+<a href="#zaharia">[Zaharia et al.]</a>, <a href="#savarese">[Savarese]</a>,
+<a href="#tdcox">[tdcox]</a>, <a href="#studer">[Studer et al.]</a>,
+<a href="#hotz-two">[Hotz, Life Cycle]</a>, <a href="#hotz-three">[Hotz, TDSP]</a>,
+<a href="#saltz">[Saltz]</a>, <a href="#serban">[Serban]</a>,
+<a href="#heiland">[Heiland et al. 2023]</a>, <a href="#nahar">[Nahar et al.]</a>,
+<a href="#mlsoftwarearchitecture">[ML software architecture]</a>,</p>
+</div>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_data_management_and_data_processing_for_ai_systems">4. Data management and data processing for AI systems</h2>
+<div class="sectionbody">
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 50%;">
+<col style="width: 50%;">
+</colgroup>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 90 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 0 min</p></td>
+</tr>
+</tbody>
+</table>
+<div class="sect2">
+<h3 id="_terms_and_concepts_4">4.1. Terms and Concepts</h3>
+<div class="paragraph">
+<p>Data acquisition, labeling, data pipeline, ETL processes, data aggregation, data cleansing, transformation, augmentation, files, RDBMS, NoSQL, data products, data contracts, data architectures: Data Warehouse, Data Lake, Data Mesh</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_learning_goals_4">4.2. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-4-1">LG 4-1: Acquiring and labeling data</h4>
+<div class="paragraph">
+<p>Participants gain an overview of various methods for acquiring and labeling data.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-2">LG 4-2: Know common platforms for publicly accessible data</h4>
+<div class="paragraph">
+<p>Participants can name common platforms for publicly accessible data.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-3">LG 4-3: Overview of relevant tools for data labeling</h4>
+<div class="paragraph">
+<p>The participants know relevant tools for data labeling such as CVAT, Amazon Mechanical Turk.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-4">LG 4-4: Designing efficient data pipelines and architectures</h4>
+<div class="paragraph">
+<p>The participants have an understanding of the design of efficient data pipelines and data architectures, and they understand how to consider data quality for storage solutions and their management.
+To this end, participants are familiar with architectural patterns for data engineering pipelines and ETL processes.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-5">LG 4-5: Know strategies for data aggregation, cleansing, transformation, enrichment and augmentation</h4>
+<div class="paragraph">
+<p>The participants know strategies for data aggregation, cleansing, transformation, enrichment and augmentation.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-6">LG 4-6: Overview of tools for data engineering pipelines</h4>
+<div class="paragraph">
+<p>Participants will gain an overview of relevant tools for data engineering pipelines such as Apache Spark and Flink.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-4-7">LG 4-7: Know the options for storing data</h4>
+<div class="paragraph">
+<p>Participants will be familiar with various options for storing data as well as their advantages and disadvantages. The options include the following technologies:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>CSV files</p>
+</li>
+<li>
+<p>Column-oriented files</p>
+</li>
+<li>
+<p>Relational and NoSQL databases</p>
+</li>
+<li>
+<p>Data warehouses</p>
+</li>
+<li>
+<p>Data Lakes</p>
+</li>
+</ul>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_references_4">4.3. References</h3>
+<div class="paragraph">
+<p><a href="#sarkis">[Sarkis]</a>, <a href="#serra">[Serra]</a>, <a href="#dehghani">[Dehghani]</a>, <a href="#reis">[Reis et al.]</a>, <a href="#bornstein">[Bornstein et al.]</a>,
+<a href="#ford">[Ford et al.]</a>, <a href="#bhajaria">[Bhajaria 2022]</a>, <a href="#sanderson">[Sanderson et al.]</a>, <a href="#jones">[Jones]</a></p>
+</div>
+<div style="page-break-after: always;"></div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="_important_quality_features_for_the_operation_of_ai_systems">5. Important quality features for the operation of AI systems</h2>
+<div class="sectionbody">
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 50%;">
+<col style="width: 50%;">
+</colgroup>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 160 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 30 min</p></td>
+</tr>
+</tbody>
+</table>
+<div class="sect2">
+<h3 id="_terms_and_concepts_5">5.1. Terms and Concepts</h3>
+<div class="paragraph">
+<p>Quality features, scalability, performance optimization, monitoring, logging, feedback, FinOps for AI platforms</p>
+</div>
+</div>
+<div class="sect2">
+<h3 id="_learning_goals_5">5.2. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-5-1">LG 5-1: Know (hardware) requirements for training and inference</h4>
+<div class="paragraph">
+<p>The participants know the different (hardware) requirements for TPU, GPU or CPU for training and inference, for example.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-2">LG 5-2: Know the trade-offs of different model architectures with regard to quality characteristics</h4>
+<div class="paragraph">
+<p>Participants will be able to name examples of trade-offs between different model architectures with regard to quality characteristics. In particular for scaling, efficiency and storage load, participants should know the trade-offs as well as the advantages and disadvantages of important architectures such as RNNs and transformers.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-3">LG 5-3: Adjust different quality features of an ML model</h4>
+<div class="paragraph">
+<p>Participants will be familiar with ways to adjust various quality characteristics such as accuracy, efficiency and storage load of an ML model and to trade them off against each other. In particular, the participants know the following techniques:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Quantization</p>
+</li>
+<li>
+<p>Pruning</p>
+</li>
+<li>
+<p>Distillation</p>
+</li>
+<li>
+<p>LoRA</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-4">LG 5-4: Understanding the costs, power consumption and sustainable use of AI (Green IT)</h4>
+<div class="paragraph">
+<p>Participants gain an understanding of the costs, power consumption and sustainable use of AI (green IT). In particular, participants will know how to develop AI models and systems that work in a resource-efficient manner by using memory, computing power and storage space efficiently.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-5">LG 5-5: Know (hardware) requirements for training and inference</h4>
+<div class="paragraph">
+<p>The participants know the term MLOps for the automation of the life cycle of a data science project and the connection with DevOps.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-6">LG 5-6: Model training, parameters, metrics and results tracking</h4>
+<div class="paragraph">
+<p>The participants have an understanding of tracking in model training, parameters, metrics and results.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-7">LG 5-7: Evaluate ML models and AI systems based on them</h4>
+<div class="paragraph">
+<p>Participants will gain an overview of approaches for evaluating ML models and AI systems based on them.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-8">LG 5-8: Know types of drift and possible causes and solutions for them</h4>
+<div class="paragraph">
+<p>Participants are familiar with different types of drift, such as data drift or model drift, as well as possible causes and solutions.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-9">LG 5-9: Overview of CI/CD pipelines, model management and deployment strategies for AI models</h4>
+<div class="paragraph">
+<p>Participants will have an understanding of CI/CD pipelines, model management and deployment strategies for AI models.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-10">LG 5-10: Know platforms for model provision</h4>
+<div class="paragraph">
+<p>The participants are familiar with common platforms for model provision, such as Huggingface Hub.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-11">LG 5-11: Classify tools for the creation of POCs of AI systems</h4>
+<div class="paragraph">
+<p>Participants will be able to name common tools for creating POCs for AI systems, such as Gradio, and understand how they work conceptually.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-12">LG 5-12: Knowing the deployment options of AI models</h4>
+<div class="paragraph">
+<p>Participants are familiar with various deployment options for AI models. Participants will understand a selection of the following options:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>API Deployment</p>
+</li>
+<li>
+<p>Embedded Deployment</p>
+</li>
+<li>
+<p>Batch Prediction</p>
+</li>
+<li>
+<p>Streaming</p>
+</li>
+<li>
+<p>Containerization</p>
+</li>
+<li>
+<p>Serverless Deployment</p>
+</li>
+<li>
+<p>Cloud services</p>
+</li>
+</ul>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-13">LG 5-13: List advantages and disadvantages of SaaS and self-hosting</h4>
+<div class="paragraph">
+<p>The participants know the advantages and disadvantages of SaaS and self-hosting and can weigh up the pros and cons.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-14">LG 5-14: Overview of SaaS AI solutions</h4>
+<div class="paragraph">
+<p>Participants will gain an overview of well-known SaaS AI solutions, such as Azure OpenAI Services.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-15">LG 5-15: Know embedded deployments of ML models</h4>
+<div class="paragraph">
+<p>Participants will be familiar with various options and standards for embedded deployments of ML models.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-16">LG 5-16: Understanding monitoring with regard to AI-specific requirements</h4>
+<div class="paragraph">
+<p>The participants understand the need for monitoring, also with regard to AI-specific requirements such as drift tracking.
+Participants are familiar with relevant metrics such as accuracy, precision, recall and F1 score, MAE, MSE, perplexity, latency, throughput and resource utilization and understand why these are relevant for monitoring.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-17">LG 5-17: Overview of sample tools for monitoring</h4>
+<div class="paragraph">
+<p>The participants know example tools for monitoring. This includes both general tools, such as Prometheus &amp; Grafana, as well as ML-specific tools, such as MLflow.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-18">LG 5-18: Understanding user feedback and methods and tools for collecting user feedback</h4>
+<div class="paragraph">
+<p>Participants understand the benefits of user feedback for further model training. In addition, participants are familiar with various methods and tools for collecting user feedback, such as choosing between multiple answers and flagging in Gradio.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-19">LG 5-19: Know methods for using feedback for model training</h4>
+<div class="paragraph">
+<p>Participants are familiar with various methods of using feedback for model training, such as RLHF, RLAIF and DPO.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-20">LG 5-20: [OPTIONAL] Understanding the MLOps pipeline using a practical example</h4>
+<div class="paragraph">
+<p>Using a practical example, participants will learn what an MLOps pipeline can look like and what insights it offers into parameters, metrics, etc.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-21">LG 5-21: [OPTIONAL] Make build vs. buy decisions for MLOps systems/components</h4>
+<div class="paragraph">
+<p>Participants will be able to make build vs. buy decisions for MLOps systems/components.</p>
+</div>
+</div>
+<div class="sect3">
+<h4 id="LG-5-22">LG 5-22: [OPTIONAL] Know MLOps tools and end-to-end platforms</h4>
+<div class="paragraph">
+<p>The participants are familiar with well-known MLOps tools and end-to-end platforms, such as:</p>
 </div>
 <div class="ulist">
 <ul>
 <li>
-<p><strong>Terms/principles</strong>: Essential core terms of this topic.</p>
+<p>Domino Data Lab, h2o.ai, DVC, activeloop, aporia, argo, arize, bentoML, comet ML, DagsHub, Databricks MLOps Stacks, Feast, Kedro, Kubeflow, Metaflow, MLflow, MLRun, prefect, PrimeHub, Weights &amp; Biases, WhyLabs, zenML, KNIME, RapidMiner, NVIDIA AI Enterprise, watsonx.ai</p>
 </li>
 <li>
-<p><strong>Teaching/practice time</strong>: Defines the minimum amount of teaching and practice time that must be spent on this topic or its practice in an accredited training course.</p>
+<p>OpenSource: MLFlow, Weights &amp; Biases, ClearML</p>
 </li>
 <li>
-<p><strong>Learning goals</strong>: Describes the content to be conveyed including its core terms and principles.</p>
+<p>PaaS: AWS SageMaker, Azure ML.</p>
 </li>
 </ul>
 </div>
-<div class="paragraph">
-<p>This section therefore also outlines the skills to be acquired in corresponding training courses.</p>
 </div>
 </div>
 <div class="sect2">
-<h3 id="_supplementary_information_terms_translations">Supplementary Information, Terms, Translations</h3>
+<h3 id="_references_5">5.3. References</h3>
 <div class="paragraph">
-<p>To the extent necessary for understanding the curriculum, we have added definitions of technical terms to the <a href="https://github.com/isaqb-org/glossary">iSAQB glossary</a> and complemented them by references to (translated) literature.</p>
+<p><a href="#chen">[Chen et al. 2022]</a>, <a href="#treveil">[Treveil et al. 2020]</a>, <a href="#haviv">[Haviv et al. 2023]</a>, <a href="#osipov">[Osipov 2022]</a>,
+<a href="#tanweihao">[Tan Wei Hao et al. 2024]</a>, <a href="#wilson">[Wilson 2022]</a>, <a href="#salama">[Salama et al.]</a>, <a href="#kumara">[Kumara et al.]</a></p>
 </div>
 <div style="page-break-after: always;"></div>
 </div>
 </div>
 </div>
 <div class="sect1">
-<h2 id="_this_is_the_first_modules_title">1. This is the First Module&#8217;s Title</h2>
+<h2 id="_system_architectures_and_platforms_for_generative_ai_systems">6. System architectures and platforms for generative AI systems</h2>
 <div class="sectionbody">
 <table class="tableblock frame-all grid-all stretch">
 <colgroup>
@@ -2305,254 +3499,287 @@ <h2 id="_this_is_the_first_modules_title">1. This is the First Module&#8217;s Ti
 </colgroup>
 <tbody>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 160 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 30 min</p></td>
 </tr>
 </tbody>
 </table>
 <div class="sect2">
-<h3 id="_terms_and_principles">1.1. Terms and Principles</h3>
+<h3 id="_terms_and_concepts_6">6.1. Terms and Concepts</h3>
 <div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<p>Generative AI, LLMs, MLflow, Managed MLflow, Azure Machine Learning, Metaflow, Generative AI, LLM, (Stable) Diffusion, Vector DB, Embedding, RNN, Transformer, RAG, Agentic Workflows etc.</p>
 </div>
 </div>
 <div class="sect2">
-<h3 id="_learning_goals">1.2. Learning Goals</h3>
+<h3 id="_learning_goals_6">6.2. Learning Goals</h3>
 <div class="sect3">
-<h4 id="LG-1-1">LG 1-1: This the first learning goal, in category xy</h4>
+<h4 id="LG-6-1">LG 6-1: Overview of integration levels of AI</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>Participants are familiar with the various levels of AI integration. These include the following levels:</p>
 </div>
+<div class="ulist">
+<ul>
+<li>
+<p>Applications (e.g. coding assistants)</p>
+</li>
+<li>
+<p>AI engineering (e.g. prompt engineering)</p>
+</li>
+<li>
+<p>ML model development (e.g. pytorch)</p>
+</li>
+<li>
+<p>ML infrastructure (e.g. vector DBs).</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_references">1.3. References</h3>
+<div class="sect3">
+<h4 id="LG-6-2">LG 6-2: Know libraries, interfaces and tools for the integration of AI models</h4>
 <div class="paragraph">
-<p><a href="#roser">[Roser 2022]</a>, <a href="#burkov">[Burkov 2019]</a>, <a href="#geron">[Géron 2022]</a>, <a href="#kelleher">[Kelleher 2015]</a>, <a href="#vaughan">[Vaughan 2020]</a>,
-<a href="#bahree">[Bahree 2024]</a>, <a href="#harvard">[Harvard et al. 2024]</a>, <a href="#dellacqua">[Dell&#8217;Acqua 2022]</a>, <a href="#visenger-one">[Visengeriyeva, JTF]</a>, <a href="#visenger-two">[Visengeriyeva, J-Curve]</a>,
-<a href="#agrawal">[Agrawal et al.]</a>, <a href="#tan">[Tan et al.]</a>, <a href="#chong">[Chong et al.]</a>, <a href="#hall">[Hall et al. 2023]</a>, <a href="#huyen">[Huyen 2022]</a>, <a href="#wang">[Wang et al. 2024]</a></p>
+<p>The participants know some examples of common libraries, interfaces and tools for the integration of AI models.</p>
 </div>
-<div style="page-break-after: always;"></div>
 </div>
+<div class="sect3">
+<h4 id="LG-6-3">LG 6-3: Integrate AI systems into the overall architecture of an IT landscape</h4>
+<div class="paragraph">
+<p>Participants know how integrate AI systems into IT landscapes at a strategic level. For example, they can use the strategic design of DDD (especially context maps) to determine and document the type and degree of integration of AI systems.</p>
 </div>
 </div>
-<div class="sect1">
-<h2 id="_heres_the_title_of_the_second_lesson">2. Here&#8217;s the Title of the Second Lesson</h2>
-<div class="sectionbody">
-<table class="tableblock frame-all grid-all stretch">
-<colgroup>
-<col style="width: 50%;">
-<col style="width: 50%;">
-</colgroup>
-<tbody>
-<tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
-</tr>
-</tbody>
-</table>
-<div class="sect2">
-<h3 id="_terms_and_principles_2">2.1. Terms and Principles</h3>
+<div class="sect3">
+<h4 id="LG-6-4">LG 6-4: Overview of relevant quality features for AI systems</h4>
 <div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<p>The participants understand the quality characteristics that are particularly relevant for AI systems. This includes the following quality characteristics in particular:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>Reliability</p>
+</li>
+<li>
+<p>Scalability</p>
+</li>
+<li>
+<p>Efficiency</p>
+</li>
+<li>
+<p>Security</p>
+</li>
+<li>
+<p>Maintainability</p>
+</li>
+<li>
+<p>Interpretability</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_learning_goals_2">2.2. Learning Goals</h3>
 <div class="sect3">
-<h4 id="LG-2-1">LG 2-1: TBD</h4>
+<h4 id="LG-6-5">LG 6-5: [OPTIONAL] Know evaluation frameworks for AI systems</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>Participants are familiar with common evaluation frameworks such as LangSmith or LangFuse to deal with indeterminacy and errors in AI systems.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="LG-2-2">LG 2-2: TBD</h4>
+<h4 id="LG-6-6">LG 6-6: [OPTIONAL] Discuss a case study with an imaginary professionalism</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>Participants will practise and discuss a case study with an imaginary technicality to weigh up integration options for AI into an existing software landscape.</p>
 </div>
 </div>
-</div>
-<div class="sect2">
-<h3 id="_references_2">2.3. References</h3>
+<div class="sect3">
+<h4 id="LG-6-7">LG 6-7: Fundamental understanding of generative AI</h4>
 <div class="paragraph">
-<p><a href="#engler">[Engler et al.]</a>, <a href="#nist">[Nist]</a>, <a href="#hall">[Hall et al. 2023]</a>, <a href="#masood">[Masood et al. 2023]</a>, <a href="#csiro">[CSIRO et al. 2023]</a>,
-<a href="#pruksachatkun">[Pruksachatkun et al. 2023]</a>, <a href="#chen">[Chen et al. 2022]</a>, <a href="#atlas">[ATLAS]</a>, <a href="#visenger-links">[Visengeriyeva, Ethics]</a>,
-<a href="#eu-ai-act">[EU AI Act]</a>, <a href="#hotz">[Hotz, Best Practices]</a>, <a href="#bhajaria">[Bhajaria 2022]</a>, <a href="#jarmul">[Jarmul 2023]</a>, <a href="#molnar">[Molnar 2024]</a></p>
+<p>The participants have a basic understanding of generative AI such as LLMs and stable diffusion.</p>
 </div>
-<div style="page-break-after: always;"></div>
 </div>
+<div class="sect3">
+<h4 id="LG-6-8">LG 6-8: Understanding how LLMs work</h4>
+<div class="paragraph">
+<p>Participants understand how LLMs work and can categorize the associated terminology: Token, Embedding, RNN, Transformer, Attention.</p>
 </div>
 </div>
-<div class="sect1">
-<h2 id="_the_third_modules_title">3. The Third Module&#8217;s Title</h2>
-<div class="sectionbody">
-<table class="tableblock frame-all grid-all stretch">
-<colgroup>
-<col style="width: 50%;">
-<col style="width: 50%;">
-</colgroup>
-<tbody>
-<tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
-</tr>
-</tbody>
-</table>
-<div class="sect2">
-<h3 id="_terms_and_principles_3">3.1. Terms and Principles</h3>
+<div class="sect3">
+<h4 id="LG-6-9">LG 6-9: Understand known patterns in the use of LLMs</h4>
 <div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<p>The participants understand known patterns for the use of LLMs. This includes the following patterns:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>RAG and retrieval strategies</p>
+</li>
+<li>
+<p>Function calling</p>
+</li>
+<li>
+<p>Finetuning</p>
+</li>
+<li>
+<p>Assistants</p>
+</li>
+<li>
+<p>Agents</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_learning_goals_3">3.2. Learning Goals</h3>
 <div class="sect3">
-<h4 id="LG-3-1">LG 3-1: TBD</h4>
+<h4 id="LG-6-10">LG 6-10: Knowing use cases for RAG (Retrieval-Augmented Generation)</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>The participants know typical use cases for RAG such as "Talk to your documents/database/API".</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="LG-3-2">LG 3-2: TBD</h4>
+<h4 id="LG-6-11">LG 6-11: Knowing and understanding selected RAG techniques</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>The participants know a selection of common RAG techniques such as:</p>
 </div>
+<div class="ulist">
+<ul>
+<li>
+<p>Simple RAG</p>
+</li>
+<li>
+<p>Context Enrichment Techniques</p>
+</li>
+<li>
+<p>Fusion Retrieval</p>
+</li>
+<li>
+<p>Intelligent Reranking</p>
+</li>
+<li>
+<p>Query Transformations</p>
+</li>
+<li>
+<p>Adaptive Retrieval</p>
+</li>
+<li>
+<p>Iterative retrieval</p>
+</li>
+<li>
+<p>Ensemble Retrieval</p>
+</li>
+<li>
+<p>Knowledge Graph Integration</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_references_3">3.3. References</h3>
+<div class="sect3">
+<h4 id="LG-6-12">LG 6-12: Know types of prompt engineering</h4>
 <div class="paragraph">
-<p><a href="#tuberlin">[TU Berlin]</a>, <a href="#bornstein">[Bornstein et al.]</a>,
-<a href="#crowe">[Crowe et al. 2024]</a>, <a href="#lakshmanan">[Lakshmanan et al.]</a>,
-<a href="#alake">[Alake]</a>, <a href="#koc">[Koc]</a>,
-<a href="#cdteliot">[Cdteliot]</a>, <a href="#visenger-three">[Visengeriyeva, AI Agents]</a>,
-<a href="#zaharia">[Zaharia et al.]</a>, <a href="#savarese">[Savarese]</a>,
-<a href="#tdcox">[tdcox]</a>, <a href="#studer">[Studer et al.]</a>,
-<a href="#hotz-two">[Hotz, Life Cycle]</a>, <a href="#hotz-three">[Hotz, TDSP]</a>,
-<a href="#saltz">[Saltz]</a>, <a href="#serban">[Serban]</a>,
-<a href="#heiland">[Heiland et al. 2023]</a>, <a href="#nahar">[Nahar et al.]</a>,
-<a href="#mlsoftwarearchitecture">[ML software architecture]</a>,</p>
-</div>
-<div style="page-break-after: always;"></div>
-</div>
+<p>The participants know different types of prompt engineering, such as</p>
 </div>
+<div class="ulist">
+<ul>
+<li>
+<p>Few-Shot-Learning</p>
+</li>
+<li>
+<p>Chain-of-Thought</p>
+</li>
+<li>
+<p>Role-Playing</p>
+</li>
+</ul>
 </div>
-<div class="sect1">
-<h2 id="_fourth_module_this_is_its_title">4. Fourth Module, This is its Title</h2>
-<div class="sectionbody">
-<table class="tableblock frame-all grid-all stretch">
-<colgroup>
-<col style="width: 50%;">
-<col style="width: 50%;">
-</colgroup>
-<tbody>
-<tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
-</tr>
-</tbody>
-</table>
-<div class="sect2">
-<h3 id="_terms_and_principles_4">4.1. Terms and Principles</h3>
 <div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<p>as well as general best practices for prompting.</p>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_learning_goals_4">4.2. Learning Goals</h3>
 <div class="sect3">
-<h4 id="LG-4-1">LG 4-1: TBD</h4>
+<h4 id="LG-6-13">LG 6-13: Overview of agentic workflows</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>Participants know what agentic workflows are and are familiar with the terms reflection, tool usage, planning and multi-agent collaboration.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="LG-4-2">LG 4-2: TBD</h4>
+<h4 id="LG-6-14">LG 6-14: Know a selection of design patterns for generative AI systems</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>The participants know which design patterns exist for generative AI systems. They know a selection of the following patterns:</p>
 </div>
+<div class="ulist">
+<ul>
+<li>
+<p>AI Query Router [Simple Router; Ranking-based Router; Learning-based Router</p>
+</li>
+<li>
+<p>Layered Caching Strategy Leading to Fine-Tuning</p>
+</li>
+<li>
+<p>Multiplexing AI Agents</p>
+</li>
+<li>
+<p>Fine-Tuning LLMs for Multiple Tasks</p>
+</li>
+<li>
+<p>Blending Rules-Based and Generative Approaches</p>
+</li>
+<li>
+<p>Utilizing Knowledge Graphs with LLMs</p>
+</li>
+<li>
+<p>Swarm of Generative AI Agents</p>
+</li>
+<li>
+<p>Modular Monolith LLM Approach with Composability</p>
+</li>
+<li>
+<p>Memory Cognition for LLMs</p>
+</li>
+<li>
+<p>Red and Blue Team Dual-Model Evaluation</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_references_4">4.3. References</h3>
+<div class="sect3">
+<h4 id="LG-6-15">LG 6-15: Know techniques for evaluating LLM applications</h4>
 <div class="paragraph">
-<p><a href="#sarkis">[Sarkis]</a>, <a href="#serra">[Serra]</a>, <a href="#dehghani">[Dehghani]</a>, <a href="#reis">[Reis et al.]</a>, <a href="#bornstein">[Bornstein et al.]</a>,
-<a href="#ford">[Ford et al.]</a>, <a href="#bhajaria">[Bhajaria 2022]</a>, <a href="#sanderson">[Sanderson et al.]</a>, <a href="#jones">[Jones]</a></p>
-</div>
-<div style="page-break-after: always;"></div>
-</div>
+<p>The participants know several techniques for evaluating LLM applications. These can be, for example, the following techniques:</p>
 </div>
-</div>
-<div class="sect1">
-<h2 id="_and_this_is_module_no_5">5. And This is Module no 5</h2>
-<div class="sectionbody">
-<table class="tableblock frame-all grid-all stretch">
-<colgroup>
-<col style="width: 50%;">
-<col style="width: 50%;">
-</colgroup>
-<tbody>
-<tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
-</tr>
-</tbody>
-</table>
-<div class="sect2">
-<h3 id="_terms_and_principles_5">5.1. Terms and Principles</h3>
-<div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<div class="ulist">
+<ul>
+<li>
+<p>Scoring</p>
+</li>
+<li>
+<p>Human feedback</p>
+</li>
+<li>
+<p>Comparative Evaluation</p>
+</li>
+<li>
+<p>Model Based Evaluation</p>
+</li>
+</ul>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_learning_goals_5">5.2. Learning Goals</h3>
 <div class="sect3">
-<h4 id="LG-5-1">LG 5-1: TBD</h4>
+<h4 id="LG-6-16">LG 6-16: Overview of known LLMs and selection criteria</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>The participants are familiar with well-known LLMs such as GPT, Claude, Gemini, Llama, Mistral or Luminous, and the selection criteria for a suitable LLM.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="LG-5-2">LG 5-2: TBD</h4>
+<h4 id="LG-6-17">LG 6-17: Understanding the importance of cost management for GenAI applications</h4>
 <div class="paragraph">
-<p>tbd.</p>
+<p>The participants understand the importance of cost management for GenAI applications.</p>
 </div>
 </div>
-</div>
-<div class="sect2">
-<h3 id="_references_5">5.3. References</h3>
+<div class="sect3">
+<h4 id="LG-6-18">LG 6-18: Give examples of common libraries, interfaces and tools related to LLM applications</h4>
 <div class="paragraph">
-<p><a href="#chen">[Chen et al. 2022]</a>, <a href="#treveil">[Treveil et al. 2020]</a>, <a href="#haviv">[Haviv et al. 2023]</a>, <a href="#osipov">[Osipov 2022]</a>,
-<a href="#tanweihao">[Tan Wei Hao et al. 2024]</a>, <a href="#wilson">[Wilson 2022]</a>, <a href="#salama">[Salama et al.]</a>, <a href="#kumara">[Kumara et al.]</a></p>
-</div>
-<div style="page-break-after: always;"></div>
+<p>The participants know some examples of common libraries, interfaces and tools in connection with LLM applications such as OpenAI-API or LangChain.</p>
 </div>
 </div>
-</div>
-<div class="sect1">
-<h2 id="_and_this_is_module_no_6">6. And This is Module no 6</h2>
-<div class="sectionbody">
-<table class="tableblock frame-all grid-all stretch">
-<colgroup>
-<col style="width: 50%;">
-<col style="width: 50%;">
-</colgroup>
-<tbody>
-<tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
-</tr>
-</tbody>
-</table>
-<div class="sect2">
-<h3 id="_terms_and_principles_6">6.1. Terms and Principles</h3>
+<div class="sect3">
+<h4 id="LG-6-19">LG 6-19: [OPTIONAL] Know agentic AI software architectures, AI agent architecture components and types of AI agent architectures</h4>
 <div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
+<p>The participants know agentic AI software architectures, AI agent architecture components, types of AI agent architectures.</p>
 </div>
 </div>
-<div class="sect2">
-<h3 id="_learning_goals_6">6.2. Learning Goals</h3>
-
 </div>
 <div class="sect2">
 <h3 id="_references_6">6.3. References</h3>
@@ -2565,7 +3792,7 @@ <h3 id="_references_6">6.3. References</h3>
 </div>
 </div>
 <div class="sect1">
-<h2 id="_and_this_is_module_no_7">7. And This is Module no 7</h2>
+<h2 id="_case_studies_and_practical_projects">7. Case studies and practical projects</h2>
 <div class="sectionbody">
 <table class="tableblock frame-all grid-all stretch">
 <colgroup>
@@ -2574,23 +3801,20 @@ <h2 id="_and_this_is_module_no_7">7. And This is Module no 7</h2>
 </colgroup>
 <tbody>
 <tr>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: XXX min</p></td>
-<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: XXX min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Duration: 110 min</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Practice time: 110 min</p></td>
 </tr>
 </tbody>
 </table>
 <div class="sect2">
-<h3 id="_terms_and_principles_7">7.1. Terms and Principles</h3>
-<div class="paragraph">
-<p>Term 1, Term 2, Term 3</p>
-</div>
-</div>
-<div class="sect2">
-<h3 id="_learning_goals_7">7.2. Learning Goals</h3>
+<h3 id="_learning_goals_7">7.1. Learning Goals</h3>
+<div class="sect3">
+<h4 id="LG-7-1">LG 7-1: [OPTIONAL] Apply the acquired knowledge in real-life scenarios using case studies and practical projects</h4>
 <div style="page-break-after: always;"></div>
 </div>
 </div>
 </div>
+</div>
 <div class="sect1">
 <h2 id="_references_7">References</h2>
 <div class="sectionbody">
@@ -2631,6 +3855,9 @@ <h2 id="_references_7">References</h2>
 <p><a id="bornstein-radovanic"></a>[bornstein-radovanic] M. Bornstein and R. Radovanovic: Emerging Architectures for LLM Applications <a href="https://a16z.com/emerging-architectures-for-llm-applications/" class="bare">https://a16z.com/emerging-architectures-for-llm-applications/</a></p>
 </li>
 <li>
+<p><a id="brynjolfsson"></a>[Brynjolfsson et al.] Brynjolfsson, E.: The Productivity J-Curve: How Intangibles complement General Purpose Technologies <a href="https://www.nber.org/system/files/working_papers/w25148/w25148.pdf" class="bare">https://www.nber.org/system/files/working_papers/w25148/w25148.pdf</a></p>
+</li>
+<li>
 <p><a id="burkov"></a>[Burkov 2019] Burkov, A.: The Hundred-Page Machine Learning Book <a href="https://themlbook.com/" class="bare">https://themlbook.com/</a></p>
 </li>
 </ul>
@@ -2918,9 +4145,6 @@ <h2 id="_references_7">References</h2>
 <p><a id="visenger-one"></a>[Visengeriyeva, JTF] Visengeriyeva, L.: Defining Jagged Technological Frontier:https://www.perplexity.ai/page/defining-jagged-technological-iF8sDPVFQEKSdd2oyytztA</p>
 </li>
 <li>
-<p><a id="visenger-two"></a>[Visengeriyeva, J-Curve] Visengeriyeva, L.: The Productivity J-Curve of AI: <a href="https://www.perplexity" class="bare">https://www.perplexity</a></p>
-</li>
-<li>
 <p><a id="visenger-three"></a>[Visengeriyeva, AI Agents] Visengeriyeva, L.: AI Agents vs. Traditional Models <a href="https://www.perplexity.ai/page/ai-agents-vs-traditional-model-JFf4gKT0RySW_Ehvbxho2g" class="bare">https://www.perplexity.ai/page/ai-agents-vs-traditional-model-JFf4gKT0RySW_Ehvbxho2g</a></p>
 </li>
 <li>
@@ -2957,8 +4181,8 @@ <h2 id="_references_7">References</h2>
 </div>
 <div id="footer">
 <div id="footer-text">
- 2024.1-rev0-EN<br>
-Last updated 2024-11-28 20:13:45 UTC
+ 2024.1-rev1-EN<br>
+Last updated 2025-02-08 19:11:46 UTC
 </div>
 </div>
 </body>
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alt="isaqb logo" width="150"></span>Certified Professional for Software Architecture<sup>&#174;</sup> (CPSA)</h1>
 <div class="details">
 <span id="author" class="author">Curriculum Software Architecture for AI Systems [SWARC4AI]</span><br>
-<span id="revnumber"> 2024.1-rev0-EN,</span>
-<span id="revdate">20241128</span>
+<span id="revnumber"> 2024.1-rev1-EN,</span>
+<span id="revdate">20250208</span>
 </div>
 </div>
 <div id="content">
@@ -1915,7 +1915,7 @@ <h1><span class="image"><img src="data:image/jpg;base64,/9j/4AAQSkZJRgABAQAAAQAB
 </div>
 </div>
 <div class="sect1">
-<h2 id="_latest_release_2024_1_rev0">Latest Release 2024.1-rev0</h2>
+<h2 id="_latest_release_2024_1_rev1">Latest Release 2024.1-rev1</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>The advanced level curriculum <strong>Software Architecture for AI Systems [SWARC4AI]</strong> is currently maintained and published in both English (EN) and German (DE). Maintainers of this curriculum and voluntary reviewers collaborate on <a href="https://github.com/isaqb-org">GitHub</a> to improve the curriculum.</p>
@@ -1951,8 +1951,8 @@ <h2 id="_latest_release_2024_1_rev0">Latest Release 2024.1-rev0</h2>
 </div>
 <div id="footer">
 <div id="footer-text">
- 2024.1-rev0-EN<br>
-Last updated 2024-11-28 20:13:45 UTC
+ 2024.1-rev1-EN<br>
+Last updated 2025-02-08 19:11:46 UTC
 </div>
 </div>
 </body>