Course Conclusion

Developed with
Calculated Systems

Contents

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The course is part of these learning paths

Start Modelling Data with Amazon SageMaker
course-steps
2
certification
1
lab-steps
3
Getting Started with Machine Learning Models
course-steps
2
certification
1
lab-steps
5
AWS Machine Learning – Specialty Certification Preparation
course-steps
39
certification
14
lab-steps
15
Start course
Overview
Difficulty
Intermediate
Duration
31m
Students
412
Ratings
5/5
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Description

This course takes an introductory look at using the SageMaker platform, specifically within the context of preparing data, building and deploying machine learning models.

During this course, you'll gain a practical understanding of the steps required to build and deploy these models along with learning how SageMaker can simplify this process by handling a lot of the heavy lifting both on the model management side, data manipulation side, and other general quality of life tools.

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

Learning Objectives

  • Obtain a foundational understanding of SageMaker
  • Prepare data for use in a machine learning project
  • Build, train, and deploy a machine learning model using SageMaker

Intended Audience

This course is ideal for data scientists, data engineers, or anyone who wants to get started with Amazon SageMaker.

Prerequisites

To get the most out of this course, you should have some experience with data engineering and machine learning concepts, as well as familiarity with the AWS platform.

Transcript

SageMaker is at its core a platform for managing all parts of the machine learning pipeline. This is everything from labeling and establishing training data, exploring the data with a notebook, and determining the correct model, training the models at scale, and even servicing it once it's in production.

It's also very important to remember that notebooks alone, even outside of the machine learning pipeline are extremely useful in running Python and other data-heavy applications on AWS. I'm not joking when I say I use SageMaker notebooks pretty much preferably over running on my local machine, that way my projects don't bleed together and I have a good amount of isolation when I'm programming and Python.

So as always, I hope you've enjoyed this introductory course to SageMaker. Feel free to leave feedback and check out the other classes that we've created particularly in the data engineering and data science learning paths. We have a ton of labs and lectures that focus on SageMaker and the ways to use SageMaker and I really hope you enjoy them. Thank you.

About the Author
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Calculated Systems
Training Provider
Students
5734
Labs
31
Courses
13
Learning Paths
17

Calculated Systems was founded by experts in Hadoop, Google Cloud and AWS. Calculated Systems enables code-free capture, mapping and transformation of data in the cloud based on Apache NiFi, an open source project originally developed within the NSA. Calculated Systems accelerates time to market for new innovations while maintaining data integrity.  With cloud automation tools, deep industry expertise, and experience productionalizing workloads development cycles are cut down to a fraction of their normal time. The ability to quickly develop large scale data ingestion and processing  decreases the risk companies face in long development cycles. Calculated Systems is one of the industry leaders in Big Data transformation and education of these complex technologies.