- Using distributed computing: Distributed computing frameworks such as Apache Spark and Hadoop can enable parallel processing and reduce training times.
- Using more efficient algorithms: Researchers are developing more efficient algorithms and models that can reduce computational requirements and training times.
- Using data augmentation: Data augmentation techniques can increase the size and diversity of the training dataset, reducing the need for large-scale data collection and preprocessing.
By exploring these strategies, researchers and practitioners can reduce the costs associated with training multimodal AI models and make them more accessible and practical for a wider range of applications.