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 the training data

First of all, you can use multiple AWS services to prepare data for machine learning, such as EMR, Redshift, Glue, and so on. After preprocessing the training data, you should store it in S3, in a format expected by the algorithm you are using. The following table shows the list of acceptable data formats per algorithm:

Figure 7.1 – Data formats that are acceptable per AWS algorithm

As we can see, many algorithms accept text/.csv format. Keep in mind that you should follow these rules if you want to use that format:

  • Your CSV file can't have a header record.
  • For supervised learning, the target variable must be in the first column.
  • While configuring the training pipeline, set the input data channel as content_type equal to text/csv.
  • For unsupervised learning, set the label_size within the content_type to 'content_type=text/csv;label_size=0'.

Although text/.csv format is fine for many use...