To mitigate these costs, researchers and practitioners are exploring various strategies, such as:
- Using transfer learning: Pre-trained models can be fine-tuned on smaller datasets, reducing the need for large-scale training.
- Using cloud-based services: Cloud-based services such as Google cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning can provide access to scalable computing resources and reduce the need for on-premises infrastructure.