It looks like there are different routes to deploying an ML model on SageMaker. You can:
pre-train a model, create a deployment archive, then deploy
create an estimator, train the model on SageMaker with a script, then deploy
My question is: are there benefits of taking the second approach? To me, it seems like writing a training script would require a bit of trial and error and perhaps some extra work to package it all up neatly. Why not just train a model running cells sequentially in a jupyter notebook where I can track each step, and then go with the first approach?
Does anyone have experience and can compare/contrast these approaches?