Fundamentals of AI Quality – Getting Started with Trustworthy Artificial Intelligence
Acquire the fundamentals of quality assurance and prepare for the AIQCP exam
Unlock the potential of artificial intelligence (AI) while ensuring its quality and trustworthiness with our comprehensive 2-day training course on the Fundamentals of AI Quality – Getting Started with Trustworthy Artificial Intelligence. This course is designed to equip you with the essential knowledge and skills to manage AI quality effectively and prepare for the AIQCP exam.
Artificial intelligence opens up unimagined possibilities for companies but also entails considerable risks. To fully exploit the potential of this technology, these risks must be managed, and the desired AI quality must be ensured. Implementing a management system for AI quality provides a solid foundation for responsible AI use within your organization.
This training will introduce you to a comprehensive framework for AI quality based on best practices, legal requirements, and standards, considering the latest developments in international regulatory activities. You will learn the maturity level a company must reach to ensure AI system quality, avoid AI risks, and maintain a reliable data pipeline. All essential aspects influencing the trustworthiness of AI applications or products are examined. A case study will help you understand the practical applicability of the AI Quality Framework.
Join us for this essential 2-day training course to master the fundamentals of AI quality and ensure your organization can leverage AI responsibly and effectively. Enroll today and take the first step towards becoming a certified AI quality management professional!
We look forward to your participation!
This training program is aimed at professionals who want to understand, control, and manage the quality of AI. It is suitable for individuals involved in projects at any stage of the AI life cycle, from conception to decommissioning:
- Managing Director and IT Manager
- AI and software engineers, data analysts, software developers
- Compliance and Quality Manager
- Consultants and all AI subject matter experts who supervise AI projects
- Importance of AI quality
- The broad definition of AI
- Challenges of AI adoption
- AI-specific risks
- The risk-based framework for AI quality
- The 6 pillars of AI quality:
security, cybersecurity, compliance,
ethics, performance, sustainability - Risk assessment
- AI Quality Maturity Analysis
- The 6 pillars of AI quality:
- AI readiness of the organization
- AI governance requirements
- Expectations of the context
- Compliance:
Legal requirements, standards and ongoing regulatory activities - AI Strategy
- Required skills and competencies
- Application example: Maturity profile of a company
- AI Systems
- AI Reference Architecture:
Core Areas (Data Quality, AI Models, AI Training),
Integration (Execution, Control Flow),
Monitoring (Testing and Control) - Infrastructure
- Application example: Maturity analysis of an AI system
- AI Reference Architecture:
- AI Processes
- AI Lifecycle: Phases and Q-Gates
- Data Lifecycle: Phases and Q-Gates
- Interaction between AI and data lifecycle
- Risk management over the entire life cycle
- Validation and Verification:
Methods for AI Explainability - Cybersecurity:
AI Security, Attacks and Defense - Application example: Maturity profile of AI processes
- Planning an
AI Quality Management System- Roadmap to launch
The training course provides participants with basic knowledge about why AI quality is important, how AI quality can be defined, an overview of current and upcoming standards and regulations, stakeholder roles, responsibilities, and liabilities throughout the AI life cycle. It is based on current and upcoming standards, regulations, and best practices from industry and academia and ensures that internationally accepted methodologies are applied to achieve high-quality, trustworthy AI. All necessary aspects of AI quality, including safety, security, legal, ethics, performance, and sustainability, are covered. AIQCP is the key to developing the right competencies for AI quality management.
Key Topics Covered
- Importance of AI Quality: Understand why AI quality is crucial for scaling AI, complying with regulations, and demonstrating responsible AI use.
- Defining AI Quality: Learn how AI quality can be defined and managed.
- Standards and Regulations: Get an overview of current and upcoming standards and regulations.
- Stakeholder Roles and Responsibilities: Understand the roles, responsibilities, and liabilities throughout the AI lifecycle.
- Trustworthiness of AI: Explore all necessary aspects of AI quality, including safety, security, legal, ethics, performance, and sustainability.
By participating in this training course, you will:
- Develop AI Quality Management Systems: Actively participate in developing an AI quality management system.
- Manage Quality and Risk: Manage quality and risk at all stages of the AI lifecycle.
- Avoid Mistakes in AI Adoption: Identify potential mistakes in AI adoption and avoid them.
- Stay Updated on Legal Requirements: Familiarize yourself with the current status of legal and normative requirements.
- Learn from Experts: Our experts, actively involved in AI standardization and regulation, will guide you through the course.
- Earn a Certificate: After completing the modules you will receive a certificate of participation. And by passing the examination, receive a certificate proving your skills in AI and data quality management.
This training is conducted by an instructor in a virtual classroom, which means that the course is live online. Participants will learn through online teaching methods, including lectures, case studies, group exercises, discussions, problem solving, and examples with explanations. Additionally, there will be assignments and quizzes during the virtual classroom training. Participants should connect to the class from any location with internet access.
Each module is delivered live using webinar technology, creating a virtual classroom learning environment. Live sessions provide direct access to the trainer, allowing participants to ask questions, understand complex concepts, and share ideas with peers. It is REQUIRED to have a webcam and microphone in order to interact with the instructor and other participants.
The course content and structure are designed by the domain experts from TÜV SÜD. With immense experience and knowledge in the relevant standards, our team of product specialists and technical experts at TÜV SÜD, developed the course content based on current business landscape and market requirements
Participants will learn through role plays, case studies, group exercises, scenarios and discussions and will receive a Certificate of participation from the TÜV SÜD Academy at the end of the AI Quality course.
Certification: Certification is possible after passing the exam. If required, please book the required multiple-choice exam "AI Quality Engineer – TÜV" (Level 1)
None, but basic knowledge of common machine learning algorithms is recommended.