A company wants to reuse existing pretrained domain models instead of training models from scratch to solve new but related tasks. Which ML strategy should they use?

Transfer learning is the correct strategy because it involves taking a model pretrained on a large dataset for a general task and fine-tuning it for a new, related task with a smaller dataset. This leverages the knowledge gained by the pretrained model, significantly reducing training time and data requirements compared to training from scratch. Increasing or decreasing the number of training epochs are hyperparameter adjustments within a training process, not a strategy for reusing pretrained models. Unsupervised learning is a different paradigm where models learn patterns from unlabeled data, which doesn't directly address the goal of reusing existing pretrained models for new, related tasks.