Data Quality Training

TÜV SÜD’s exclusive data quality training workshop helps you discover the art of crafting impeccable datasets for AI applications. Created by industry expert Daniel Kondermann and systems and safety specialist Ruben Strenzke, this comprehensive session is tailored for professionals in advanced driver assistance systems and autonomous driving.

This data quality course delves into the intricacies of how you derive dataset requirements that cover all your stakeholders’ needs, and how you define taxonomies. We will navigate the complexities of data annotation, accuracy, and quality assurance with hands-on exercises and real-world scenarios.

Data quality training gives you knowledge about key industry standards shaping the automotive industry. You will learn to define precise dataset requirements, and optimise data for machine learning algorithms. You will gain invaluable insights into enhancing the quality and efficacy of your data.

The workshop will give you a comprehensive overview of computer vision dataset generation. It will help you avoid pitfalls in computer vision data management, and enable you to learn about best practices from industry experts.

This training is relevant to a variety of professionals, including directors, engineering managers, product owners, systems engineers, safety engineers, and other decision makers responsible for developing computer vision-based products.

TÜV SÜD has deep expertise and competence in AI. We have an up-to-date understanding all relevant existing and upcoming standards and regulations to empower your data quality training.

Our experts who conduct our courses and issue data quality certification are actively involved in standardisation, regulation, and practical implementation of AI quality management systems.

Inspiring trust in technology has been our DNA and has served us as a guiding principle since our inception. Translating this into today’s digital world, we ensure the safety, security, reliability, and robustness of AI systems.

Our data quality courses cover the following topics:

• Dataset design overview
• Taxonomy definition (Workshop)
• Requirements engineering
• Data annotation
• Data accuracy
• Data quality assurance methods
• Data collection planning
• Summary and wrap-up

Prerequisites: Fundamental knowledge in machine learning, computer vision and product management.

Language: EN (training and material), German on request.

Course Duration: 8 UE, 1 day.

Certification: Certification of attendance.

Mode of Delivery: Virtual classroom with online Trainer, alternative of presentation and workshops/activities, onsite possible on demand.