Amazon SageMaker Notebook Instances - Amazon SageMaker
MaintenanceMachine Learning with the SageMaker Python SDK

Amazon SageMaker Notebook Instances

An Amazon SageMaker notebook instance is a machine learning (ML) compute instance running the Jupyter Notebook application. One of the best ways for machine learning (ML) practitioners to use Amazon SageMaker is to train and deploy ML models using SageMaker notebook instances. The SageMaker notebook instances help create the environment by initiating Jupyter servers on Amazon Elastic Compute Cloud (Amazon EC2) and providing preconfigured kernels with the following packages: the Amazon SageMaker Python SDK, AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), Conda, Pandas, deep learning framework libraries, and other libraries for data science and machine learning.

Use Jupyter notebooks in your notebook instance to:

  • prepare and process data

  • write code to train models

  • deploy models to SageMaker hosting

  • test or validate your models

SageMaker also provides sample notebooks that contain complete code examples. These examples show how to use SageMaker to do common ML tasks. For more information, see Access example notebooks.

For information about pricing with Amazon SageMaker notebook instance, see Amazon SageMaker Pricing.

Maintenance

SageMaker updates the underlying software for Amazon SageMaker Notebook Instances at least once every 90 days. Some maintenance updates, such as operating system upgrades, may require your application to be taken offline for a short period of time. It is not possible to perform any operations during this period while the underlying software is being updated. We recommend that you restart your notebooks at least once every 30 days to automatically consume patches.

For more information, contact AWS Support.

Machine Learning with the SageMaker Python SDK

To train, validate, deploy, and evaluate an ML model in a SageMaker notebook instance, use the SageMaker Python SDK. The SageMaker Python SDK abstracts AWS SDK for Python (Boto3) and SageMaker API operations. It enables you to integrate with and orchestrate other AWS services, such as Amazon Simple Storage Service (Amazon S3) for saving data and model artifacts, Amazon Elastic Container Registry (ECR) for importing and servicing the ML models, Amazon Elastic Compute Cloud (Amazon EC2) for training and inference.

You can also take advantage of SageMaker features that help you deal with every stage of a complete ML cycle: data labeling, data preprocessing, model training, model deployment, evaluation on prediction performance, and monitoring the quality of model in production.

If you're a first-time SageMaker user, we recommend you to use the SageMaker Python SDK, following the end-to-end ML tutorial. To find the open source documentation, see the Amazon SageMaker Python SDK.

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