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AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

You're reading from  AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

Product type Book
Published in Mar 2021
Publisher Packt
ISBN-13 9781800569003
Pages 338 pages
Edition 1st Edition
Languages
Authors (2):
Somanath Nanda Somanath Nanda
Profile icon Somanath Nanda
Weslley Moura Weslley Moura
Profile icon Weslley Moura
View More author details

Table of Contents (14) Chapters

Preface Section 1: Introduction to Machine Learning
Chapter 1: Machine Learning Fundamentals Chapter 2: AWS Application Services for AI/ML Section 2: Data Engineering and Exploratory Data Analysis
Chapter 3: Data Preparation and Transformation Chapter 4: Understanding and Visualizing Data Chapter 5: AWS Services for Data Storing Chapter 6: AWS Services for Data Processing Section 3: Data Modeling
Chapter 7: Applying Machine Learning Algorithms Chapter 8: Evaluating and Optimizing Models Chapter 9: Amazon SageMaker Modeling Other Books You May Enjoy

Choosing instance types in Amazon SageMaker

SageMaker is a pay-for-usage model. There is no minimum fee for it.

When we think about instances on SageMaker, it all starts with an EC2 instance. This instance is responsible for all your processing. It's a managed EC2 instance. These instances won't show up in the EC2 console and cannot be SSHed either. The instance type starts with ml.

SageMaker offers instances of the following families:

  • The t family: This is a burstable CPU family. With this family, you get a normal ratio of CPU and memory. This means that if you have a long-running training job, then you lose performance over time as you spend the CPU credits. If you have very small jobs, then they are cost-effective. For example, if you want a notebook instance to launch training jobs, then this family is the most relevant and cost-effective.
  • The m family: In the previous family, we saw that CPU credits are consumed faster due to their burstable nature...
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