How to manage 3 billion miles of driving data?
Sponsored by: SiaSearch
- Automated Vehicles
- Database Management
Date: 18 February
Time: 2PM London/3PM CET
Introducing a new way to automate manual data handling for good.
Automated vehicles generate up to 10 TB of data per hour and robust autonomous driving starts with understanding this data in depth. In order to leverage the sensor data, engineers spent up to 75% of their time manually exploring, curating and selecting this data. Today, they cannot rely on common tools, because the raw data is unstructured, which prohibits the use of status quo databases. As a result productivity and progress are much lower than would be necessary.
In this free webinar, Clemens Viernickel and Mark Pfeiffer, the Co-Founders of SiaSearch, will discuss how raw data masses can be made easily accessible through automatic indexing and content-based search.
We will explore in depth the use of advanced tooling for understanding, visualizing, curating, and collaborating on your data — allowing teams to work twice as fast to build better software via a powerful interface and API.
Register for the webinar today to learn more about the idea, technology and vision behind SiaSearch, which could help you build better models and data sets, with less effort.
Co-Founder & CEO
Clemens is the CEO of SiaSearch, a data management company building the first data platform for hyper scale unstructured sensor data. Clemens also serves as a lead member of the mobility working group in the German Federal AI association. Before that, he helped to build up Merantix, Europe’s leading AI venture studio, as head of operations and later entrepreneur in residence. Previously, Clemens held a position as senior associate in Google’s European Central Strategy team, supporting the leadership with core business processes and long term planning. Clemens holds a degree from University of St. Gallen and studied at Sciences Po Paris as well as the Hertie School.
Co-Founder & CTO
Mark Pfeiffer is the co-founder and CTO of SiaSearch. He is a graduate of ETH Zurich, where he also received his Ph.D. in the area of intelligent ground robot navigation in dynamic urban environments, leveraging data-driven technologies. He has held a visiting position at the University of California Berkeley and gained work experience at BMW.
Key Learning Objectives
- Understand how for data-driven technologies, intelligent data handling makes all the difference
- How to use metadata and advanced tooling to explore, understand and curate data for efficient ADAS and autonomous vehicle development
- Improve model performance by identifying biases in your data and discovering rare sequences
- Building a data architecture and database that can scale linearly as data continues to grow
- How use APIs to transform tedious trial and error data review into a magical data playground
- Automated Driving
- Head of R&D
- VP R&D
- Director of R&D
- R&D lead
- R&D Engineer
- Senior R&D Engineer
- Validation Engineer
- Verification Engineer
- Validation Specialist
- Director of Data Annotation and Labeling Department
- Head of Data Annotation and Labeling
- Manager ADAS
- Perception Engineer