Amazon SageMaker Notebook Playground

Lab Steps

Logging in to the Amazon Web Services Console
Opening JupyterLab on Your SageMaker Notebook
Selecting a Playground Notebook

Ready for the real environment experience?

Time Limit2h


Amazon SageMaker notebooks provide a fully-managed environment for machine learning and data science development. This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you. There are hundreds of notebooks to choose from. Example topics covered in the sample notebooks include:

  • Machine learning basics
  • Popular machine learning libraries including Tensorflow, PyTorch, Scikit-Learn, and Apache MXNet
  • DeepAR forecasting of energy consumption 
  • Image classification
  • Reinforcement learning for portfolio optimization
  • Sentiment analysis of IMDB movie reviews
  • Clustering similar United States counties using population date
  • SageMaker debugging
  • and much more!

You may also use the playground to develop your own machine learning models and gain hands-on experience with SageMaker.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Use SageMaker notebook instances to learn about SageMaker and machine learning concepts
  • Experiment with SageMaker notebooks

Intended Audience

This lab is intended for:

  • Anyone interested in SageMaker or machine learning


You should be familiar with:

  • Some knowledge of machine learning concepts is beneficial, but not required
  • Basic programming using Python 3. The Introduction to Python learning path is useful for meeting this requirement. 
  • Basic programming using R (if you select any of the R notebooks)
About the Author
Learning paths16

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.