
Your Model Was Accurate Last Year. Is It Still?
Here’s a problem that doesn’t always make it into model deployment conversations: AI models degrade over time, even when you don’t touch them. Not because the model changes. Because the
Booth 21-25 | AI Data Management Zone | Tokyo Big Sight

Here’s a problem that doesn’t always make it into model deployment conversations: AI models degrade over time, even when you don’t touch them. Not because the model changes. Because the

Speed benchmarks in AI research can feel detached from anything practical. A paper claims a 10x speedup and you skim past it because the benchmarks never quite match the thing you’re actually trying to do. The FlexGNN result — up to 95.5x faster training on large graphs — is worth
