Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
Section 1: Introduction to Machine Learning
Section 2: Data Engineering and Exploratory Data Analysis
Section 3: Data Modeling

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...