[zsyllepsis]

In my experience SageMaker was relatively straightforward for fine-tuning models that could fit on a single instance, but distributed training still requires a good bit of detailed understanding of how things work under the covers. SageMaker Jumpstart includes some pretty easy out-of-the-box configurations for fine-tuning models that are a good starting point. They will incorporate some basic quantization and other cost-savings techniques to help reduce the total compute time.

To help control costs, you can choose pretty conservative settings in terms of how long you want to let the model train for. Once that iteration is done and you have a model artifact saved, you can always pick back up and perform more rounds of training using the previous checkpoint as a starting point.