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Start Modelling Data with Amazon SageMaker

Intermediate
Up to 5h 59m
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Training content

2
3
1
COURSE
Intermediate
Duration: 31 minutes and 25 seconds
Introduction to SageMaker
This lesson provides a practical understanding of the steps required to build and deploy machine learning models using Amazon SageMaker.
COURSE
Beginner
Duration: 32 minutes and 16 seconds
Get Started with Amazon SageMaker Data Wrangler, Data Pipeline, Feature Store and Ground Truth
Get started with the latest SageMaker Data Wrangler, Data Pipeline and Feature Store services (released at re:invent Dec 2020) and SageMaker Ground Truth
HANDS-ON LAB
Intermediate
Duration: Up to 1 hour
Using SageMaker Notebooks to Train and Deploy Machine Learning Models
In this lab, you'll use a SageMaker notebook to learn how to write Python code to prepare data, train and deploy models, and use them for real-time inference.
HANDS-ON LAB
Beginner
Duration: Up to 2 hours
Amazon SageMaker Notebook Playground
This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you.
HANDS-ON LAB
Beginner
Duration: Up to 1 hour and 30 minutes
Forecast Flight Delays with Amazon SageMaker
This lab uses Amazon SageMaker to create a machine learning model that forecasts flight delays for US domestic flights.
EXAM
Duration: Up to 25 minutes
Final Exam: Start Modeling Data with Amazon SageMaker
Final Exam: Start Modeling Data with Amazon SageMaker

This course provides you with an introduction to the Amazon SageMaker service followed by the opportunity to practice using SageMaker in a series of practical hands-on labs. 

Learning Objectives

  • Understand the key services of Amazon SageMaker
  • Learn how to use training data sets with machine learning models in SageMaker
  • Understand how machine learning concepts can be applied to real-world scenarios

Intended Audience

This course is intended for anyone who is:

  • Interested in understanding how to deploy machine learning models on a managed service like Amazon SageMaker
  • Looking to enrich their understanding of machine learning and how to use it to solve complex problems
  • Looking to build a foundation for continued learning in the machine learning space and data science in general

Prerequisites

To get the most out of this course, you should have a general understanding of data concepts as well as some familiarity with Amazon Web Services. Some experience in data or development is preferable but not essential. 

Feedback

If you have any feedback relating to this course, feel free to contact us at support@cloudacademy.com. 

Your certificate for this learning path
Calculated Systems
This content is developed in partnership with Calculated Systems
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About the Author

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Andrew Larkin, opens in a new tab
Head of Content
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Andrew is fanatical about helping business teams gain the maximum ROI possible from adopting, using, and optimizing Public Cloud Services. Having built  70+ Cloud Academy courses, Andrew has helped over 50,000 students master cloud computing by sharing the skills and experiences he gained during 20+  years leading digital teams in code and consulting. Before joining Cloud Academy, Andrew worked for AWS and for AWS technology partners Ooyala and Adobe.

Covered Topics

Amazon Web Services
Artificial Intelligence for AWS
Amazon SageMaker
Machine Learning
Development
Artificial Intelligence
Python