How Do Teams Handle Dataset Quality At Scale For AI Projects?

I’ve been spending more time thinking about the dataset side of AI development and wondering where most teams encounter the biggest challenges.

A lot of discussions focus on model architecture and training techniques, but many production issues seem to trace back to the data itself:

• inconsistent annotations between labelers
• difficulty collecting rare edge cases
• balancing dataset diversity without introducing noise
• maintaining quality as datasets grow larger
• keeping training data aligned with real deployment environments

For those who work with datasets regularly:
• What is your biggest bottleneck today?
• How do you measure annotation quality?
• At what scale do dataset management problems become significant?

Interested in hearing real-world experiences from people dealing with data collection, labeling, and dataset maintenance.