The next revolution in computer vision isn’t about bigger models or better architectures. It’s about something far more fundamental: data quality. Yet here’s the dirty little secret about visual data quality: most teams are wasting precious time fine-tuning hyperparameters and tweaking architectures when it’s their data that is the problem.
We’ve partnered with
Coursera and
UC Davis to create a hands-on course about a systematic, data-centric approach that transforms how you use data to evaluate and improve your models. Gain in-depth knowledge and practical skills on how to:
Build systematic processes for data completeness and consistency checks
Implement robust data labeling workflows with built-in quality control
Create robust data pre-processing pipelines