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
The “more data wins” era is mostly over. What’s replacing it is messier and more interesting: a stack of techniques — fine-tuning, preference optimization, retrieval — each with its own cost curve, its own failure modes, and its own demands on data quality. Pick the wrong lever and you can
