Yes, you can use spot instances. I recommend it, and always run training on spot instances. If you are using the Python SDK, add the following parameters to your Estimator: ``` use_spot_instances=True, max_run={maximum runtime here}, max_wait={maximum wait time}, checkpoint_s3_uri={URI of your bucket and folder }, ``` See the documentation for more details here: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html As far as instance types are concerned, the individual algorithms contain some initial recommendations for instances types: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html For example, see the EC2 Instance Recommendation for the Image Classification Algorithm: https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html There was a presentation at re:Invent 2020 - How to choose the right instance type for ML inference: https://www.youtube.com/watch?v=0DSgXTN7ehg Hope this helps

https://stackoverflow.com/questions/60429339/update-live-aws-sagemaker-auto-scaled-endpoint-instance-type-without-putting-it

The instance types you are seeing are Fast Launch Instances ( which are instance types designed to launch in under two minutes). In order to see all the types of instances, click on the switch on top of the instance type list that says "Fast Launch", that should display the rest of available instances. Here is additional info about fast launch instances: https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html Hope it helps!