-
Certificates: Certificate "Artificial intelligence: AI expert"
-
Additional Certificates: Certificate "AI representative with TÜV Rheinland-certified qualification"
Certificate "AI Manager with TÜV Rheinland certified qualification"
Certificate "AI auditor with TÜV Rheinland-certified qualification" -
Examination: Praxisbezogene Projektarbeiten mit Abschlusspräsentationen
KI-Beauftragte:r mit TÜV Rheinland geprüfter Qualifikation
KI-Manager:in mit TÜV Rheinland geprüfter Qualifikation
KI-Auditor:in mit TÜV Rheinland geprüfter Qualifikation -
Teaching Times: Full-timeMonday to Friday from 8:30 a.m. to 3:35 p.m. (in weeks with public holidays from 8:30 a.m. to 5:10 p.m.)
-
Language of Instruction: German
-
Duration: 12 Weeks
Artificial intelligence: AI officers with TÜV Rheinland-certified qualifications
Role of AI officers in the compliance context (approx. 1 day)
Introduction to the role of AI officers
Tasks and responsibilities
Differentiation from other roles
Introduction to professional communication with AI (approx. 1 day)
Basics, strategies and types of prompting
Key components for effective prompting
Building an internal prompt library
Legal basics (approx. 2 days)
Overview of relevant laws and regulations: GDPR, AI Act, product liability, copyright law
In-depth study: EU AI Act (approx. 2 days)
Structure, objectives, classification
Risk classes
Obligations for suppliers, operators, importers and distributors
Conformity assessment
Documentation and transparency obligations
Post-market monitoring and reporting obligations
Management systems and standards (approx. 2 days)
Overview of ISO/IEC 42001
Importance of AI management systems (AIMS)
Integration into existing management systems (e.g. ISO 9001, ISO 27001)
Compliance strategies
Governance frameworks
Risk management, data protection and ethics (approx. 3 days)
Types of risk (bias, errors, ethical risks)
Strategic risk assessment
Risk matrix
Excursus: Risks of AI agents
Action planning
Data protection and data security for AI systems
Data ethics and transparency requirements
Quality assurance in AI projects (approx. 3 days)
Quality assurance and acceptance processes
Stakeholder analysis
Communication strategies
Training concepts for employees
Data management (approx. 1 day)
Basics of data quality
Principles of data integrity
Development of data management structures
Change management (approx. 2 days)
Dealing with resistance during AI introductions
Training concepts for employees
Development of a change management plan
Excursus: Strategic AI roadmap
Project work, certification preparation and certification exam "AI representative with TÜV Rheinland-certified qualification" (approx. 3 days)
Artificial intelligence: AI manager with TÜV Rheinland-certified qualification
Fundamentals of operational AI projects (approx. 5 days)
Introduction to AI, ML, DL, NLP and computer vision (operational focus)
Roles and tasks: Setting up, operating and reviewing the effectiveness of the management system in accordance with ISO 42001
Role delineation and collaboration: AI officer, AI manager and AI auditor
Identification and evaluation of operational use cases in the company
Project initiation: target definition, scope, feasibility analysis
Stakeholder management
Value creation and ROI through AI
Successful AI initiatives in management
Data management and use of tools (approx. 3 days)
Data preparation, quality and integration
Selection and implementation of AI tools and platforms
Practical prompting for text, image and video applications
Building simple data pipelines
Introduction to MLOps concepts
AI automation options in operation
Model training, validation and use (approx. 2 days)
Training and validation of models
Test procedures: Black box, white box, unit tests
Use of models
Monitoring and iterative optimization
Integration of AI agents in projects
Risk management and quality assurance (approx. 2 days)
Technical risk analysis: bias metrics, fairness tests, model error analysis
Quality assurance: KPIs, monitoring, acceptance processes
Management system according to ISO 42001
Security and explainability of AI systems
Operational project management and agile methods (approx. 2 days)
Agile methods: Scrum, Kanban, iterative deployment cycles
Resource and budget planning
Team and stakeholder communication
Ongoing optimization and problem-solving strategies (CIP)
Cooperation with external partners
Organizational development, governance and change management (approx. 3 days)
Analysis of business processes
Maturity level analysis, GAP analysis
Creation of an AI roadmap
AI governance and strategy development
Development of a sustainable organizational structure
Responsibilities
Practical handling of resistance in AI operations
Sustainability and corporate digital responsibility (CDR)
Project work, certification preparation and certification exam "AI Manager with TÜV Rheinland certified qualification" (approx. 3 days)
Artificial intelligence: AI auditor with TÜV Rheinland-certified qualification
Basics and framework conditions (approx. 4 days)
Roles, tasks and responsibilities within AI audits
Differentiation from other roles (e.g. AI manager)
Audit principles according to ISO 19011 (objectivity, independence, transparency)
Normative basis: ISO/IEC 42001 - structure, requirements, evidence
EU AI Act - relevant provisions for auditors
National guidelines and industry-specific standards
PDCA cycle in the audit context
Audit types: system, process, product and compliance audits
Stage 1 and Stage 2 at a glance
Documentation requirements and verification
AI-specific risks as audit objects (bias, explainability, robustness, security, data quality)
Prompting in the audit context
Regulatory and technical test criteria (approx. 4 days)
AI-specific compliance requirements
Data protection (GDPR and industry-specific requirements)
Security of AI systems (cybersecurity, access control)
Quality requirements for training and test data
Model validation and verification
Explainability and transparency of AI decisions
Performance metrics (accuracy, precision, recall, robustness)
Ethical principles and fairness
Additional industry-specific standards (e.g. ISO 13485, ISO 26262, BaFin guidelines)
Audit planning and methodology (approx. 3 days)
Definition of audit objects and objectives
Creation of an audit plan (resources, schedule, roles, communication)
Creation of questionnaires and checklists
Risk and relevance assessment of audit points
Selection of suitable audit methods (questioning, document review, technical tests)
Specifics of risk and method assessment for agent-based AI systems
Definition of supporting documents and types of evidence
Audit implementation (approx. 3 days)
Document review (Stage 1) - Requirements for AI documentation
Interview techniques and conducting discussions during the audit
On-site audit (Stage 2) - Use of audit tools
Carrying out technical tests (black box, white box, stress tests)
Use of technical tools (audit software, log analysis, code review)
Collection, validation and structuring of audit documents
Evaluation and report (approx. 2 days)
Creation of an audit report using prompting
Risk-appropriate presentation of weak points
Suggested measures and follow-up strategies
Project work, certification preparation and certification exam "AI auditor with TÜV Rheinland certified qualification" (approx. 3 days)
Changes are possible. The course content is updated regularly.
After the course, you will be able to take responsibility for the safe, ethical and effective use of artificial intelligence. You will be able to identify the business potential of AI, successfully implement change management processes and select suitable tools to strategically plan, operationally manage and sustainably anchor AI projects within the company and drive forward the digital transformation. You will also have the skills to plan AI audits in line with standards, coordinate them across departments and successfully integrate them into existing management and audit processes.
The course is aimed at auditors, specialists and managers from all areas of the company as well as project managers and employees from IT, project, process and quality management or digitization projects who want to implement and coordinate operational AI projects, audit AI systems and AI management systems or prepare for certification according to ISO/IEC 42001.
Specialists and managers who want to drive their companies forward in the digital transformation and can use AI as a tool to improve efficiency, decision-making and innovation in companies are in demand in all sectors.
Your meaningful certificate provides a detailed insight into the qualifications you have acquired and improves your career prospects.
Didactic concept
Your lecturers are highly qualified both professionally and didactically and will teach you from the first to the last day (no self-study system).
You will learn in effective small groups. The courses usually consist of 6 to 25 participants. The general lessons are supplemented by numerous practical exercises in all course modules. The practice phase is an important part of the course, as it is during this time that you process what you have just learned and gain confidence and routine in its application. The final section of the course involves a project, a case study or a final exam.
Virtual classroom alfaview®
Lessons take place using modern alfaview® video technology - either from the comfort of your own home or at our premises at Bildungszentrum. The entire course can see each other face-to-face via alfaview®, communicate with each other in lip-sync voice quality and work on joint projects. Of course, you can also see and talk to your connected trainers live at any time and you will be taught by your lecturers in real time for the entire duration of the course. The lessons are not e-learning, but real live face-to-face lessons via video technology.
The courses at alfatraining are funded by Agentur für Arbeit and are certified in accordance with the AZAV approval regulation. When submitting a Bildungsgutscheinor Aktivierungs- und Vermittlungsgutschein, the entire course costs are usually covered by your funding body.
Funding is also possible via Europäischen Sozialfonds (ESF), Deutsche Rentenversicherung (DRV) or regional funding programs. As a regular soldier, you have the option of attending further training courses via Berufsförderungsdienst (BFD). Companies can also have their employees qualified via funding from Agentur für Arbeit (Qualifizierungschancengesetz).