Introducing Kaggle Models

Kaggle Models represent one of the latest innovations on the Kaggle platform. This feature gained prominence in particular after the introduction of code competitions, where participants often train models either on their local hardware or in the cloud. Post-training, they upload these models to Kaggle as a dataset. This practice allows Kagglers to utilize these pre-trained models in their inference notebooks, streamlining the process for code competition submissions. This method significantly reduces the runtime of the inference notebooks, fitting within the stringent time and memory constraints of the competition. Kaggle’s endorsement of this approach aligns well with real-world production systems, where model training and inference typically occur in separate pipelines.

This strategy becomes indispensable with large-scale models, such as those based on Transformer architectures, considering the immense computational resources required for fine...