Unverified50% confidenceFactExact time
When the distribution of online data deviates from the training data, model performance degrades (data drift and model decay), requiring retraining to be triggered.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified训练-部署鸿沟指模型因训练与部署环境的数据分布偏移、软件依赖版本差异或硬件指令集不同而导致的性能下降79% similarUnverifiedOverfitting refers to a model performing well on training data but poorly on new data.77% similarUnverifiedDomain Shift refers to the problem where a model performs well on training data but degrades significantly after being deployed to a new environment77% similarUnverifiedTraditional draft models are trained on narrow distributions targeting a single model, causing acceptance rates to drop sharply when workloads change, and requiring the entire pre-training process to be redone each time a target model is replaced.76% similarUnverifiedContinual training is a method of continuously training on newly added data to keep the model's knowledge up to date.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/911242API
curl https://kongchang.com/api/v1/knowledge/claims/911242MCP
get_claim(id=911242)