Automate SageMaker Notebook Scripts
An end-to-end example using Lambda function, Lifecycle configuration to deploy Jupyter notebook in SageMaker
In this article, I will show you how to use Lambda functions, Lifecycle configurations to deploy SageMaker notebooks, allowing you to execute notebooks per your schedule.
This article includes the following:
- Introduction
- Prerequisite
- Examples of detailed steps
Introduction
Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models and directly deploy them into a production-ready hosted environment. One of the most powerful features is Jupyter authoring notebook instance to easily access your data sources for exploration and analysis and common machine learning algorithms. As data scientists, we can deploy models using End Point API. However, sometimes, we may only want to execute our notebook script timely and with as less engineering work as possible. Today, I will introduce how to use the Lambda function and Lifecycle configuration to deploy your notebook script as scheduled/triggered.
