What will you learn from the course?
- Importance and impact of AI in various industries and societies.
- Challenges and failures in AI and strategies to address them.
- AI system architecture and design principles.
- Identification of AI stakeholders and their roles.
- AI quality model: safety security legal compliance ethics and performance.
- Risk assessment for AI implementation.
- AI governance process framework and controls.
- AI data quality and data life cycle management.
- Application of AI quality frameworks in daily operations.
- AI standards and regulatory activities.
Course Content
AIQCP Foundation: Level 1 — Engineer
1. Motivation and requirements
- Introduction to AI and the need for AI quality
- Current and upcoming standards and regulation of AI
- Roles responsibilities and liabilities
2. AI quality
- Terms and definitions
- Key pillars of AI quality
- The AI quality life cycle
- AI quality management
3. AI quality framework
- Methodology and key elements to establish an AI quality management system
- AI governance requirements
- AI quality management in a case study
- IEEE AI ethics
4. Applying AI framework
- Plan an AI quality management system
- Control of AI quality parameters
- Testing and assessing AI
5. Examination
- Multiple Choice Test
Schedule: AIQCP Level 1
|
Region |
Dates |
Training Time (Timezone) |
|
|
Europe / Middle East |
Training: |
16 – 18 Oct |
CEST: 0900hrs – 1300hrs |
|
Examination: |
18 Oct |
CEST: 1400hrs – 1500hrs |
|
|
ASEAN |
Training: |
13 – 15 Nov |
SGT: 1300hrs – 1700hrs |
|
Examination: |
15 Nov |
SGT: 1700hrs – 1800hrs |
|
AIQCP Professional: Level 2 — Practitioner
This training builds on the AIQCP Engineer Level 1 and enables to apply the AI quality framework to concrete AI products and systems. Participants will also gain an overview of the latest developments in regulations and standardisation. A minimum of 1 year of industry experience in AI quality management are beneficial.
Participants aims at taking over AI quality responsibilities
Course content:
1. Recap of AI quality
- AI quality framework concepts
- Relevant standards and regulations
- Application methodology tools and templates
- Qualitative and quantitative metrics
- IEEE AI ethics
2. Application of AI quality framework
- Introduction to use cases
- Step-by-step implementation process
- Design of an individual AI QMS use case
3. Analysis
- Presentation and discussion of use case
- Refinement of use case based on lessons learned
- Compiling of an AI quality report for the use case
4. Examination
- Multiple Choice Test
- Design AI QMS to given use case
What is the course methodology?
Participants will learn through lectures case studies group exercises and discussions.
Who should take the course?
This programme is aimed at professionals who want to understand control and manage quality of AI and are involved in projects at any stage of the AI life cycle from conception to decommissioning:
- AI Developers and Engineers
- AI Practitioners and Data Architects
- Data Engineers and Scientists
- Quality and Technology Managers
- Software Developers and Engineers
- System Administrators
Who is the course advisor?
The course content and structure are designed by top TÜV SÜD experts for AI Quality.
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.
Benefits
What are the benefits of enrolling in this course?
Frequently asked questions
How do I enroll for the AIQCP?
What is the AIQCP methodology?
Can I get a refund if I cancel my enrollment?
Can I also attend E-learning courses on top of attending the AIQCP instructor-led course?
Upon completion of the AIQCP would I get a certificate for completion?
Who developed the AIQCP and what are their qualifications?
