The AI Engineer's Guide to Surviving the EU AI Act - Helion

ebook

Autor: Larysa Visengeriyeva
ISBN: 9781098172459
stron: 278, Format: ebook
Data wydania: 2025-06-30
Księgarnia: Helion

Cena książki: 220,15 zł (poprzednio: 255,99 zł)
Oszczędzasz: 14% (-35,84 zł)

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With the introduction of the EU AI Act, companies employing AI systems face a new set of comprehensive and stringent regulations. Dr. Larysa Visengeriyeva offers a much-needed guide for navigating these unfamiliar regulatory waters to help you meet compliance challenges with confidence.

From explaining the legislative framework to sharing strategies for implementing robust MLOps and data governance practices, this wide-ranging book shows you the way to thrive, not just survive, under the EU AI Act. It's an indispensable tool for engineers, data scientists, and policymakers engaged in or planning for AI deployments within the EU.

By reading, you'll gain:

An in-depth understanding of the EU AI Act, including the four risk categories and what they mean for you
Strategies for compliance, including practical approaches to achieving technical readiness
Actionable advice on applying MLOps methodologies to ensure ongoing compliance
Insights on the implications of the EU's pioneering approach to AI regulation and its global effects

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Spis treści

The AI Engineer's Guide to Surviving the EU AI Act eBook -- spis treści

Preface
- Who Should Read This Book
- Navigating This Book
- Conventions Used in This Book
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Understanding the AI Regulations
- The Motivation for the EU AI Act: Trustworthy AI
  - Human Agency and Oversight
  - Technical Robustness and Safety
  - Privacy and Data Governance
  - Transparency
  - Diversity, Non-Discrimination, and Fairness
  - Societal and Environmental Well-Being
  - Accountability
- The EU AI Act in a Nutshell
  - Definitions
  - Key Players from Creation to Market Operation
  - Classification of AI Systems by Risk Levels
  - Enforcement and Implementation
  - The Full Picture of Compliance
  - Penalties for EU AI Act Violation
- Existing AI Regulations and Standards
- Conclusion
- Further Reading
2. AI Engineering: A Proactive Compliance Catalyst
- Structuring the AI System Development Process with CRISP-ML(Q)
  - Risk Mitigation, Quality Assurance, and Alignment with the EU AI Act
  - The Six Phases of CRISP-ML(Q)
    - Business and data understanding
    - Data preparation
    - Model engineering
    - Model evaluation
    - Model deployment
    - Model monitoring and maintenance
- Understanding MLOps
- Defining Technical Components with the MLOps Stack Canvas
  - Value Proposition
  - Data and Code Management
    - Data Sources and Data Versioning
    - Data Analysis and Experiment Management
    - Feature Store and Workflows
    - Foundations (Reflecting DevOps)
  - Model Management
    - CI/CT/CD: ML Pipeline Orchestration
    - Model Registry and Model Versioning
    - Model Deployment
    - Prediction Serving
    - Model, Data, and Application Monitoring
  - Metadata Management
  - Additional Considerations of MLOps
- CRISP-ML(Q) and MLOps
- Conclusion
3. Data and AI Governance and AI Engineering
- The Importance of Data and AI Governance in the EU AI Act Era
- Overview of Data Governance
  - Data Governance Defined
  - The Data Engineering Lifecycle and Data Governance
    - Data generation
    - Ingestion
    - Transformation
    - Storage
    - Serving
  - Integrating Data Governance into MLOps
    - Value Proposition
    - Data Sources and Data Versioning
    - Data Analysis and Experiment Management
    - Feature Store and Workflows
    - Foundations (Reflecting DevOps)
    - CI/CT/CD: ML Pipeline Orchestration
    - Model Registry and Model Versioning
    - Model Deployment
    - Prediction Serving
    - Model, Data, and Application Monitoring
    - Metadata Management
- Overview of AI Governance
  - AI Governance Defined
  - The AI System Lifecycle and AI Governance
- Emerging Trends in Data and AI Governance
- Conclusion
4. AI System Assessment and Tailoring AI Engineering for Different Risk Levels
- AI Compliance, Governance, and Risk Management
- Creating an AI System Inventory
- Applicability of the EU AI Act
- Unacceptable RiskProhibited AI Practices
  - Prohibited AI Use Cases
  - Deep Dive: Social Scoring
- Determining EU AI Act Obligations
  - Framework for Classification of AI Systems by Risk Levels
    - High risk
      - EU AI Act high-risk system questionnaire
      - Interpretation guide
    - Limited risk (transparency obligations)
      - EU AI Act limited-risk system questionnaire
      - Interpretation guide
    - Low risk
  - Deployer or Provider
- Integrating EU AI Act Engineering Throughout the AI Development Lifecycle
- Emerging Roles in Organizations for EU AI Act Compliance
- Conclusion
5. AI Engineering for High-Risk AI Systems
- AI Engineering for the EU AI Act
  - Goals
  - Alignment with CRISP-ML(Q) Phases
- AI Engineering Practices for Achieving Compliance
  - Article 9: Risk Management System
    - 1. Business and data understanding
      - Failure Mode and Effects Analysis
      - Testability and value alignment
    - 2. Data preparation
    - 3. Modeling
    - 4. Evaluation
      - Risk identification
      - Testability
      - Value alignment
    - 5. Deployment
      - Risk identification
      - Testability
      - Value alignment
    - 6. Monitoring and maintenance
    - Summary
    - Checklist for compliance
    - Further reading
  - Article 10: Data and Data Governance
    - Key tools
    - Checklist for compliance
  - Article 11: Technical Documentation and Article 12: Record-Keeping
    - Technical documentation requirements
    - Managing documentation debt
    - Existing frameworks for documenting data and AI systems
    - Article 12: Recordkeeping requirements for high-risk AI systems
    - Interdependence of Articles 11 and 12
    - Data and AI system metadata
    - Checklist for compliance
    - Further reading
  - Article 13: Transparency and Provision of Information to Deployers and Article 14: Human Oversight
    - Article 13 quality attributes
    - Article 14 quality attributes
    - Checklist for compliance
  - Article 15: Accuracy, Robustness, and Cybersecurity
    - Checklist for compliance
    - Further information
- Conclusion
6. AI Engineering for Limited-Risk AI Systems
- Compliance Assessment Versus Transparency Obligation
- Understanding Transparency Obligations
- Aligning AI Engineering with SMACTR and CRISP-ML(Q) for Transparency
  - The Five Stages of the SMACTR Framework
  - How the SMACTR Framework Aligns with the EU AI Act
  - Business and Data Understanding Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
  - Data Preparation Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
  - Modeling Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
    - Technical implementation guide
  - Evaluation Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
    - Available tools and technologies
  - Deployment Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
    - Available tools and technologies
  - Monitoring and Maintenance Phase
    - CRISP-ML(Q) activities
    - SMACTR integration
    - Key artifacts
    - Available tools and technologies
- Technology Trend: AI Governance Platforms
  - Leading Platforms
  - Further Reading
- Conclusion
7. Toward Trustworthy General-Purpose AI and Generative AI
- The EU AI Act and Generative AI
  - GPAI Systems and Transparency Obligations
  - Regulating General-Purpose AI
    - Definition and scope
    - Systemic risk criteria
    - Obligations for providers of GPAI models
    - Additional obligations for providers of GPAI models with systemic risk
    - Obligations for deployers of GPAI models
- Predictive ML Versus GPAI
- GenAIOpsOperationalizing EU AI Act Compliance for GPAI
  - Key Components
  - How GenAIOps Extends MLOps
  - GenAIOps Tools, Workflows, and Frameworks to Support Transparency
- Aligning AI Engineering with SMACTR and CRISP-ML(Q) for Transparency
  - Business and Data Understanding Phase
    - Implementation
    - SMACTR integration
  - Data Preparation Phase
    - Implementation
    - SMACTR integration
  - Modeling Phase
    - Implementation
    - SMACTR integration
  - Evaluation Phase
    - Implementation
    - SMACTR integration
  - Deployment Phase
    - Implementation
    - SMACTR integration
  - Model Monitoring and Maintenance Phase
    - Implementation
    - SMACTR integration
- Conclusion
- Final Words and Future of AI Policymaking
A. Designing AI-Powered Applications
- Backward Thinking for Designing AI Systems
- The Machine Learning Canvas
  - Value Proposition
  - Monitoring
  - The Prediction Phase
    - Prediction Task
    - Decisions
    - Making Predictions
    - Impact Simulation
  - The Training Phase
    - Data Sources
    - Data Collection
    - Building Models
    - Features
B. Data Products and Data Contracts
- Data Products
- Data Contracts
C. The Integration of AI Governance and MLOps
- Value Proposition
- Data Sources and Data Versioning
- Data Analysis and Experiment Management
- Feature Store and Workflows
- CI/CT/CD: ML Pipeline Orchestration
- Model Registry and Model Versioning
- Model Deployment
- Prediction Serving
- Model, Data, and Application Monitoring
- Metadata Management
- Integrating Data and AI Governance into MLOps
D. Emerging Roles in Organizations for EU AI Act Compliance
- Emerging Roles
- EU AI Compliance for ML Teams
  - Team Topologies for ML Teams
  - Aligning Team Topologies with Ethics, Compliance, and Governance Roles
    - Enabling team: AI ethics and governance
    - Complicated subsystem team: Data and AI security and auditing
Index