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

Storing columnar data on Amazon Redshift

Amazon Redshift is not used for real-time transaction use, but it is used for data warehouse purposes. It is designed to support huge volumes of data at a petabyte scale. It is a column-based database used for analytics purpose, long-term processing, tending, and aggregation. Redshift Spectrum can be used to query data on S3 without loading data to the Redshift cluster (a Redshift cluster is required though). It's not an OLTP, but an OLAP. AWS QuickSight can be integrated with Redshift for visualization, with a SQL-like interface that allows you to connect using JDBC/ODBC connections for querying the data.

Redshift uses a clustered architecture in one AZ in a VPC with faster network connectivity between the nodes. It is not high availability by design as it is tightly coupled to the AZ. A Redshift cluster has a Leader Node, and this node is responsible for all the communication between the client and the computing nodes of the cluster...