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)
1
Section 1: Introduction to Machine Learning
4
Section 2: Data Engineering and Exploratory Data Analysis
9
Section 3: Data Modeling

Securing SageMaker notebooks

If you are reading this section of the chapter, then you have already learned how to use notebook instances, which type of training instances should be chosen, and how to configure and use endpoints. Now, let's learn about securing those instances. The following aspects will help to secure the instances:

  • Encryption: When we say or think about securing via encryption, then it is all about the data. But what does this mean? It means protecting data at rest using encryption, protecting data in transit with encryption, and using KMS for better role separation and internet traffic privacy through TLS 1.2 encryption. SageMaker instances can be launched with encrypted volumes by using an AWS-managed KMS key. This helps you to secure the Jupyter Notebook server by default.
  • Root access: When a user opens a shell terminal from the Jupyter Web UI, they will be logged in as ec2-user, which is the default username in Amazon Linux. Now the user can run...