Using SageMaker Notebooks to Train and Deploy Machine Learning Models

Lab Steps

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Logging in to the Amazon Web Services Console
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Opening JupyterLab on Your SageMaker Notebook
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Working Through the Lab's JupyterLab Notebook

The hands-on lab is part of this learning path

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DifficultyIntermediate
Time Limit1h
Students25
Ratings
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Description

Amazon SageMaker notebooks provide a fully-managed environment for machine learning and data science development. You will use a SageMaker notebook instance to train and deploy a machine learning model using Python. You will go through the process of preparing raw data for use with machine learning algorithms. Then you will use a built-in SageMaker algorithm to train a model using the prepared data. Lastly, you will use SageMaker to host the trained model and learn how you can make real-time predictions using the model.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Use SageMaker notebook instances to run Jupyter Notebooks
  • Write code using the Python Data Analysis Library (pandas) and the SageMaker Python SDK to:
    • train models using built-in SageMaker algorithms
    • Create SageMaker models
    • Deploy SageMaker endpoints to get real-time inferences from your models

Intended Audience

This lab is intended for:

  • Anyone interested in using SageMaker to build and deploy machine learning models in code

Prerequisites

You should be familiar with:

  • Some knowledge of machine learning concepts is beneficial, but not required
  • Basic programming using Python 3
  • Completion of the Forecast Flight Delays with Amazon SageMaker lab is recommended for a deeper understanding of the data used in this lab 
  • Basic S3 concepts
Environment before
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Environment after
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About the Author

Students42084
Labs105
Courses11
Learning paths9

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.