Start Modelling Data with Amazon SageMaker

Developed with Calculated Systems
OverviewStepsAuthor
Calculated Systems
This content is developed in partnership with Calculated Systems
DifficultyIntermediate
AVG Duration4h
Students74
Ratings
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Content
Course Created with Sketch. 2 Exams Created with Sketch. 1 Labs Created with Sketch. 3

Description

This learning path 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 learning path 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 learning path, 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

Certificate

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Learning Path Steps

1courses

This course provides a practical understanding of the steps required to build and deploy machine learning models using Amazon SageMaker.

2courses

Get started with the latest SageMaker Data Wrangler, Data Pipeline and Feature Store services (released at re:invent Dec 2020) and SageMaker Ground Truth

3labs

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.

4labs

This playground lab allows you to choose from Amazon's curated library of sample notebooks to learn about what is most important to you.

5labs

This lab uses Amazon SageMaker to create a machine learning model that forecasts flight delays for US domestic flights.

6exam-filled

Final Exam: Start Modeling Data with Amazon SageMaker

About the Author
Students97163
Courses98
Learning paths78

Head of Content

Andrew is an AWS certified professional who is passionate about helping others learn how to use and gain benefit from AWS technologies. Andrew has worked for AWS and for AWS technology partners Ooyala and Adobe.  His favorite Amazon leadership principle is "Customer Obsession" as everything AWS starts with the customer. Passions around work are cycling and surfing, and having a laugh about the lessons learnt trying to launch two daughters and a few start ups.