AI QUALITY CERTIFICATION PROGRAM (AIQCP)

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

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WHAT YOU'LL GAIN BY ENROLLING FOR THIS COURSE?

Globally recognised Training Certificate for AI Quality
Access to our community of experts for strategic guidance and continuous improvement
Understand how to successfully apply a quality management system for AI
Learning through case studies & exercises

ABOUT AI QUALITY CERTIFICATION PROGRAM (AIQCP)

The AI Quality Certification Program (AIQCP) is a certified training program that provides individuals and organisations with the necessary knowledge and skills to effectively manage the quality of AI systems and products across all organisational functions and industries.

This program has two levels and focuses on the assessment and assurance of AI quality, comprising safety, security, legal, ethics, performance, and sustainability aspects. Level 1 provides foundational knowledge on AI quality, while Level 2 concentrates on the practical implementation of AI quality management systems.

Participants in this program, led by AI experts actively involved in standardisation, regulation, and practical implementation of AI quality management systems, will gain valuable insights into internationally accepted methodologies, standards, regulations, and best practices for assuring the quality of AI.

The course provides course materials, templates, and guidance for implementing AI quality management systems. Upon completion and successful examination, participants will be awarded a certificate, proving their competence and ability to assume AI and data quality management responsibilities within their organisations.

This course is a vital foundation for scaling AI, complying with regulations, and demonstrating responsible AI use to mitigate financial, legal, and reputational risks.

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
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

TRAINING DURATION

AI Quality Training (Expert): 3 Days Instructor-led training

WHO SHOULD ATTEND?

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

PRE-REQUISITES

Possess familiarity with basic machine learning concepts (high-level)

LEARNING & CAREER BENEFITS

Learn how to differentiate technology that is governed by AI standards and regulations
Be well-equipped to manage risks and opportunities in AI
Know how to identify attack vectors such as adversarial attacks and how to eliminate them
Be able to 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
Learning through case studies & exercises

Wish to learn more about our AI QUALITY CERTIFICATION PROGRAM (AIQCP)?

CONTACT US

For further queries, please contact us at [email protected]