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

Writing to Amazon Aurora with multi-master capabilities

Amazon Aurora is the most reliable relational database engine developed by Amazon to deliver speed in a simple and cost-effective manner. Aurora uses a cluster of single primary instances and zero or more replicas. Aurora's replicas can give you the advantage of both read replicas and multi-AZ instances in RDS. Aurora uses a shared cluster volume for storage and is available to all compute instances of the cluster (a maximum of 64 TiB). This allows the Aurora cluster to provision faster and improves availability and performance. Aurora uses SSD-based storage, which provides high IOPS and low latency. Aurora does not ask you to allocate storage, unlike other RDS instances; it is based on the storage that you use.

Aurora clusters have multiple endpoints, including Cluster Endpoint and Reader Endpoint. If there are zero replicas, then the cluster endpoint is the same as the reader endpoint. If there are replicas available...