AWS SageMaker Notebook Development Service

An AWS SageMaker Notebook Development Service is a model authoring service within AWS SageMaker (a fully managed end-to-end machine learning service).

• Context:
  • It can be used to create Trained SageMaker Models.
  • It can operate on an AWS SageMaker Notebook Instance, such as at [1]
  • It can be integrated with other AWS SageMaker components, such as: SageMaker Model Training Service, SageMaker Model Hosting Service, ...
• Example(s):
  • AWS SageMaker Authoring Service (2018-11-02).
  • AWS SageMaker Authoring Service (2017-11-29).
  • …
• Counter-Example(s):
  • AWS Machine Learning.
  • AWS RDS.
  • Azure ML Studio.
  • H2O ML Platform.
• See: Jupyter Notebook, Anaconda Enterprise.

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References

2018a

• http://aws.amazon.com/blogs/aws/sagemaker/
  • QUOTE: Authoring: Zero-setup hosted Jupyter notebook IDEs for data exploration, cleaning, and preprocessing. You can run these on general instance types or GPU powered instances. …

    … When I create a notebook instance it launches an ML compute instance that comes with Anaconda packages and libraries common in deep learning, a 5GB ML storage volume, and several example notebooks demonstrating various algorithms. I can optionally configure VPC support which creates an ENI in my VPC for easy and secure access to my resources.

2017

• https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-notebooks-instances.html
  • QUOTE: …

    Attaches An ML storage volume — Attaches a 5 GB ML storage to the ML Compute instance. You can use the volume to clean up the training dataset or to temporarily store other data to work with. There is also 20 GB of instance storage available in the /tmp directory of each Notebook Instance. This is not persistent storage and anything in it will be removed when the instance is stopped or restarted.

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