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09a-sage-intro.md

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Sagemaker

Note really like any other service. Could spend all your time in there without having to do anything else. Fully managed service from start to finish. End-to-end

Three stages

Build: Preprocess, ground truth, notebooks

Train: Built-in algorithms, hyperparameter tuning, notebooks, infrastructure

Deploy: Realtime, batch, notebooks, infrastructure, Neo

Control

Console, SDK, Jupyter

Sagemaker API: Can be called to provision and run services

Sagemaker Python SDK: Control and provision Sagemaker instances right through Jupyter Notebooks

Sagemaker notebooks

Instance type doesn't have to be very large in order for you to do intense machine learning.

  • Instance type can be used as a control panel for the notebook, and the notebook calls services that provision more intense infrastructure to run the code
  • Lots of example notebooks to browse
  • Starting notebooks takes a while as it's provisioning all the infrastructure in the backend
  • Clicking Open Jupyter | Open Jupyterlab redirects you to the endpoint for your instance with a pre-signed url.
    • Can't share the link with others