For choosing a SageMaker hosted notebook type:

Do you plan to do all of your preprocessing of your data in-memory on the notebook, or do you plan to orchestrate ETL with external services?

If you're planning to load the dataset into memory on the notebook instance for exploration/preprocessing, the primary bottleneck here would be ensuring the instance has enough memory for your dataset. This would require at least the 16gb types (.xlarge) (full list of ML instance types available here). Further, depending on how compute intensive your pre-processing is, and your desired pre-processing completion time, you can opt for a compute optimized instance (c4, c5) to speed this up.

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For the training job, specifically:

Using the Amazon SageMaker SDK, your training data will be loaded and distributed to the training cluster, allowing your training job to be completely separate from the instance your hosted notebook is running on.

Figuring out the ideal instance type for training will depend on whether your algorithm of choice/training job is memory, CPU, or IO bound. Since your dataset will likely be loaded onto your training cluster from S3, the instance you choose for your hosted notebook will have no bearing on the speed of your training job.

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Broadly: When it comes to SageMaker notebooks, the best practice is to use your notebook as a "puppeteer" or orchestrator, that calls out to external services (AWS Glue or Amazon EMR for preprocessing, SageMaker for training, S3 for storage, etc). It is best to treat them as ephemeral forms of compute/storage for building and kicking off your experiment pipeline.

This will allow you to more closely pair compute, storage, and hosting resources/services with the demands for your workload, ultimately resulting in the best bang for your buck by not having you pay for latent or unused resources.

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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!

I'll address your questions for both SageMaker Studio JupyterLab and SageMaker Notebook Instances: A) SageMaker Studio JupyterLab: 1. SageMaker Studio is billed per second of usage, with a minimum of 1 minute. This means if you use Studio for just one minute, you'll be charged for that minute, not a full hour. The billing starts when you launch the SageMaker Studio application and ends when you log out or when an administrator ends your session. 2. In SageMaker Studio, each user gets their own dedicated workspace instance. If multiple data scientists are working in Studio, they would each have their own session and be billed separately. There isn't a concept of sharing the same Studio session among multiple users in the way you described. B) SageMaker Notebook Instance: 1. SageMaker Notebook Instances are also billed per second of usage, with a minimum of 1 minute. Like Studio, if you use a Notebook Instance for just one minute, you'll be charged for that minute, not a full hour. 2. For Notebook Instances, multiple users can technically share the same instance, but it's not designed for simultaneous use by multiple users. If two users are using the same Notebook Instance, you would still be charged based on the instance type and the total time it's running, not per user. So in your example with an ml.t3.16xlarge instance used for one hour, the charge would be $1 (assuming that's the correct rate), regardless of whether one or two users were using it. It's important to note that while sharing a Notebook Instance is possible, it's generally not recommended for collaborative work due to potential conflicts and security concerns. SageMaker Studio is better suited for multi-user environments, where each user gets their own workspace. In both cases, you only pay for the compute resources you use, and there are no additional charges for using the SageMaker Studio or Notebook Instance interfaces themselves. **Sources** Amazon SageMaker Studio pricing - Amazon SageMaker AI Machine Learning Service – Amazon SageMaker Pricing – AWS Community | Enhancing ML Efficiency with Amazon SageMaker