In autonomous vehicle development, data isn’t a static asset but a constantly evolving resource. This presentation challenges the notion that more data equals better models, arguing that iteration and refinement are more critical than sheer volume. We’ll explore why datasets for machine learning must adapt over time, and how to source the right data when future needs are unpredictable.
In this session, you will discover why:
- Datasets are evolving assets, requiring continuous refinement
- Smaller, high-quality data can outperform large, unfocused datasets
- Strategies for sourcing relevant data are in an uncertain landscape
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