AI Quality Training Course

In this training course, organisational leaders will comprehend their roles, responsibilities and liabilities and will be enabled to plan their AI development and adoption strategy.

1. Motivation
   • Introduction to AI and the need for AI quality
   • Current and upcoming regulation of AI 
   • Roles, responsibilities and liabilities
2. AI quality framework to establish trust in AI and scale AI applications
   • Compliance of AI systems and services
   • Readiness of organisations as developer and adopter
   • Performance metrics and industry best practices
3. Path towards AI quality
   • Skills and responsibilities - Train your AI leads and assign roles to implement AI quality
   • Assess your organisation’s AI quality readiness
   • Stepwise implementation of AI quality framework

This AI Quality certification program is a 3-level certified personnel training program for the assessment and assurance of the quality of AI systems and products across all industries. This training course will enable participants to analyse and implement AI quality management systems for organisations, whether they are suppliers, providers, users or maintainers of AI. 

AIQCP Engineer: Level 1 - Foundation

This 2-day training course equips the participant with the basic knowledge of why AI quality matters, how AI quality can be defined, and how AI quality management can be implemented. Participants also gain a general overview of current and upcoming standards and regulations, stakeholder roles, responsibilities and liabilities along the AI life cycle.

Course Content:

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

4. Applying AI framework

• Plan an AI quality management system
• Control of AI quality parameters
• Testing and assessing AI

5. Examination

• Multiple choice test

With the AIQCP Engineer certificate the candidate proves the basic knowledge and understanding of AI quality concepts.

AIQCP Professional: Level 2 - Intermediate

This 2.5-day training builds on the AIQCP Engineer Level 1 and enables the participant to apply the AI quality framework to concrete AI products and systems and respective organisations. Participants will also gain an overview of the latest developments in regulations and standardisation. A minimum of 2 years of industry experience in AI quality management are required.

Course Content:

1.Recap of AI quality

• AI quality framework concepts
• Relevant standards and regulations
• Application methodology, tools and templates
• Qualitative and quantitative metrics

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

With the AIQCP Professional certificate the candidate proves comprehensive knowledge of AI quality, the ability to implement an AI QMS in practice and the skills to take over AI quality responsibilities.

AIQCP Expert: Level 3 - Expert

This 2.5-day training builds on AIQCP Professional Level 2 and enables the participant to design and implement an AI quality framework into an organisation along the whole AI life cycle. A minimum of 3 years of industry experience in AI quality management with at least 2 implementation projects in a responsible position are required. 

Course Content:

1. AI quality framework for experts

• Deep dive into obligatory and voluntary AI quality requirements
• Updates on AI standards and regulations
• Quantitative and qualitative metrics
• Documentation best practices

2. Application of AI quality framework along the life cycle

• Introduction of AI quality use cases
• Best-practice AI quality implementation process
• Analysis of AI quality controls
• Preparation of AI QMS documentation for audit

3. Review and improvement

• Presentation and discussion of use case
• Refinement of use case based on lessons learned
• Finalisation of Assessment report of use case

4. Examination

• Multiple choice test
• Design AI QMS to a given complex use case

With the AIQCP Expert certificate the candidate proves expert understanding of AI quality, the ability to implement AI QMS along the entire life cycle, and the skills to take over comprehensive AI quality responsibilities.