Alexander Kern
Alexander Kern is AI Solutions Expert at Clockworks where he works on numerous computer vision solutions that derive structured insights from raw pixel data. His work exemplifies how AI should be combined with smart engineering to deliver solutions that always and reliably work in production.
Session
Your model isn't the problem. Your data is.
In production computer vision, teams consistently reach for model-level fixes, bigger backbones, longer training runs, more data, while the underlying dataset stays messy, mislabelled, and unrepresentative of the real world. We did the same, until we stopped.
This talk presents three data strategies we applied to a single, deceptively simple problem: counting crates moving through a logistics environment. The problem looked straightforward. The data was not.
The three strategies are:
- Annotation quality at scale: using model-assisted labelling and embedding-based clustering to systematically surface and correct errors hiding in production datasets
- Synthetic data generation: covering rare but critical edge cases absent from real captures, and navigating the domain gap between synthetic and real images
- Intelligent dataset reduction: selecting maximally informative training subsets, based on the counterintuitive principle that more data is not always better data
This is not a benchmarking talk. There are no leaderboard numbers here. The goal is to share a way of thinking about data that you can apply to your own problems, and to be honest about where each strategy works, where it struggles, and how they interact.
Target audience: Data scientists, ML engineers, and applied researchers working with real-world computer vision or ML pipelines.
Takeaways: A practical, transferable framework for approaching data quality.