Training Data Quality

COURSE OVERVIEW

Discover the art of crafting impeccable datasets for AI applications in our exclusive workshop, created by industry expert Daniel Kondermann and systems and safety specialist Ruben Strenzke. This comprehensive session, tailored for professionals in advanced driver assistance systems and autonomous driving, 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.

Join us to explore key industry standards shaping the automotive industry, learn to define precise dataset requirements, understand how to optimize data for machine learning algorithms. and gain invaluable insights into enhancing your data’s quality and efficacy. 

WHAT BENEFITS CAN YOU EXPECT FROM THIS PROGRAM?

• Obtain a comprehensive overview of computer vision dataset generation
• Avoid pitfalls in computer vision data management
• Learn about best practices from industry experts
  
  

COURSE CONTENT

• Dataset Design Overview 
• Taxonomy Definition (Workshop) 
• Requirements Engineering 
• Data Annotation
• Data Accuracy 
• Data Quality Assurance Methods 
• Data Collection Planning
• Summary and Wrap-Up 

Who Should Attend: Directors, Engineering Managers, Product Owners, Systems Engineers, Safety Engineers, and other decision makers responsible for developing computer vision-based products

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