TTA Cut Our Inference Budget by 40% When We Stopped Using It
Test-Time Augmentation (TTA) sounds great on paper: run your model on multiple transformed versions of each input, average the predictions, get better accuracy. In practice, I've watched it drain inference budgets while delivering accuracy gains so small they vanish in production noise.
Here's what actually happened when we A/B tested TTA on a defect detection model serving 200K images per day. Accuracy went from 94.2% to 95.7% — impressive, right? But inference latency jumped from 45ms to 380ms per image, our GPU costs tripled, and we ended up shutting it off after two weeks. The 1.5% accuracy gain didn't justify the operational headache.
But TTA isn't always a bad idea. There are specific scenarios where it pays off, and others where it's just burning money. Let's run both versions and see where the line is.
What TTA Actually Does (and Where It Costs You)
Continue reading the full article on TildAlice
Top comments (0)