Preface

The AWS Machine Learning Specialty certification exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus in depth using practical examples to help you with your real-world ML projects on AWS.

Starting with an introduction to ML on AWS, you will learn the fundamentals of ML and explore important AWS services for artificial intelligence (AI). You will then see how to store and process data for ML using several AWS services, such as S3 and EMR.

You will also learn how to prepare data for ML and discover different techniques for data manipulation and transformation for different types of variables. The book covers the handling of missing data and outliers and takes you through various ML tasks, such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with their specific ML algorithms, that you need to know in order to pass the exam. Finally, you will explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them.

By the end of the book, you will have gained knowledge of all the key fields of ML 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. This book is not only intended to support you in the AWS Machine Learning Specialty certification exam but also to make your ML professional journey a lot easier.

Who This Book Is for

This book is designed for both students and professionals preparing for the AWS Certified Machine Learning Specialty exam or enhance their understanding of machine learning, with a specific emphasis on AWS. Familiarity with machine learning basics and AWS services is recommended to fully benefit from this book.

What This Book Covers

Chapter 1, Machine Learning Fundamentals, covers some ML definitions, different types of modeling approaches, and all the steps necessary to build an ML product.

Chapter 2, AWS Services for Data Storage, teaches you about the AWS services used to store data for ML. You will learn about the many different S3 storage classes and when to use each of them. You will also learn how to handle data encryption and how to secure your data at rest and in transit. Finally, you will learn about other types of data store services that are also worth knowing for the exam.

Chapter 3, AWS Services for Data Migration and Processing, teaches you about the AWS services used to process data for ML. You will learn how to deal with batch and real-time processing, how to directly query data on Amazon S3, and how to create big data applications on EMR.

Chapter 4, Data Preparation and Transformation, deals with categorical and numerical features and applying different techniques to transform your data, such as one-hot encoding, binary encoding, ordinal encoding, binning, and text transformations. You will also learn how to handle missing values and outliers in your data, two important topics for building good ML models.

Chapter 5, Data Understanding and Visualization, teaches you how to select the most appropriate data visualization technique according to different variable types and business needs. You will also learn about AWS services for visualizing data.

Chapter 6, Applying Machine Learning Algorithms, covers different types of ML tasks, such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing. Each of these tasks has specific algorithms that you should know about to pass the exam. You will also learn how ensemble models work and how to deal with the curse of dimensionality.

Chapter 7, Evaluating and Optimizing Models, teaches you how to select model metrics to evaluate model results. You will also learn how to optimize your model by tuning its hyperparameters.

Chapter 8, AWS Application Services for AI/ML, covers details of the various AI/ML applications offered by AWS that you need to know about to pass the exam.

Chapter 9, Amazon SageMaker Modeling, teaches you how to spin up notebooks to work with exploratory data analysis and how to train your models on Amazon SageMaker. You will learn where and how your training data should be stored in order to make it accessible through SageMaker and explore the different data formats that you can use.

Chapter 10, Model Deployment, teaches you about several AWS model deployment options. You will review SageMaker deployment options, creating alternative pipelines with Lambda functions, working with Step Functions, configuring auto scaling, and securing SageMaker applications.

How to Use This Book

This AWS Certified Machine Learning Specialty study guide explains each concept from the exam syllabus using realistic examples and comprehensive theoretical notes. The book is your go-to resource for acing the AWS Certified Machine Learning Specialty exam with confidence.

Online Practice Resources

With this book, you will unlock unlimited access to our online exam-prep platform (Figure 0.1). This is your place to practice everything you learn in the book.

How to access the resources

To learn how to access the online resources, refer to Chapter 11, Accessing the Online Practice Resources at the end of this book.

Figure 0.1 – Online exam-prep platform on a desktop device

Sharpen your knowledge of MLS-C01 concepts with multiple sets of mock exams, interactive flashcards, and exam tips accessible from all modern web browsers.

Download the Color Images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/ky8E8.

Conventions Used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “You will use the detect_labels API from Amazon Rekognition in the code.”

A block of code is set as follows:

from sagemaker.predictor import Predictor

predictor = Predictor(endpoint_name='your-endpoint-name', sagemaker_session=sagemaker_session)

predictor.predict('input_data')

Any command-line input or output is written as follows:

sh-4.2$ cd ~/SageMaker/ sh-4.2$ git clone https://github.com/PacktPublishing/ AWS-Certified-Machine-Learning-Specialty-MLS-C01- Certification-Guide-Second-Edition.git

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: “In CloudWatch, each Lambda function will have a log group and, inside that log group, many log streams.”

Tips or important notes

Appear like this.

Get in Touch

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