Artificial Intelligence Quality Training

A practical guide to gain the required knowledge to master AI quality

A practical guide to gain the required knowledge to master AI quality


Benefitting from AI requires more than just implementing the technology. Organisations need to understand AI standards and regulations, recognise and predict the impact of AI on the business and manage the quality accordingly.


TÜV SÜD has developed training modules catered to Executives and Experts that equip organisations with skills to govern AI quality – a prerequisite to develop and adopt AI at scale. Our AI experts will guide you through quality requirements that include existing and upcoming AI standards, regulations, benchmarks, and industry best practices.


With TÜV SÜD, you can learn to:

  • differentiate technology that is governed by AI standards and regulations
  • manage risks and opportunities in AI
  • identify attack vectors such as adversarial attacks and how to eliminate them
  • define quality criteria such as robustness, accuracy, reliability, explicability and methods how to control them
  • classify AI roles, responsibilities, and liabilities for organisations and individuals 
  • specify key components of AI governance

Throughout the training, our experts will illustrate the concepts with practical real-world use cases.


our AI Training Modules


AI Quality Training – Leading the way to success (Expert– 16h)

TÜV SÜD’s AI Quality Training course for professionals developing or deploying AI systems or products provides an introduction and comprehensive overview of the governance and technical requirements to manage quality of AI systems. Our AI experts will reveal the importance of the organisational setup and enable participants to differentiate AI-specific challenges and select the respective matching AI technology for their use cases. The training will cover imperative requirements for AI system and AI data management, a risk assessment process to identify and manage relevant quality measures, essential AI quality controls, and industry best practices. The course offers participants the opportunity to receive a certificate to document skill development and organisation’s commitment to meet AI quality requirements.

Topics to be covered in the course include:

  1. The basics: Why do we need a dedicated AI Quality Framework?
    • Introduction to AI quality
    • AI technologies: Strengths and weaknesses of core algorithms and how to choose the right one
    • AI challenges and failures
    • AI reference architecture: How to describe an AI system and its boundaries
    • Case study
  2. The understanding: How can we define quality of AI?
    • Concepts of AI quality
    • AI stakeholders
    • AI system quality model: safety, security, legal, ethics, performance
    • AI process model
    • AI governance
    • AI risk assessment
    • Case study
  3. The solution: A comprehensive and compliant AI quality framework
    • AI system life cycle
    • AI data quality model
    • AI data life cycle
    • AI standards AI regulation
    • Case study
  4. The application: Applying AI quality framework in day-to-day operation
    • AI quality Controls
    • AI explicability
    • AI robustness
    • AI cybersecurity
    • AI testing
    • Case study



ASEAN                                             China                                               Middle East

India                                                 United Kingdom                               Germany

United States


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