Introduction

Machine learning (ML) is one of the most popular and rapidly growing fields in the technology industry today, with far-reaching business implications. The market for ML solutions and products is expected to grow annually by tens of billions of dollars, and with it, the demand for professionals who understand how to analyze data and build ML solutions is expected to grow as well.

ML is a highly technical field, and successful ML professionals need a foundation in mathematics, statistics, and data analysis. They must be able to code and have a fundamental understanding of infrastructure and software development best practices. In the past, the practitioners of machine learning were academics and PhDs, but the industry demand for ML is much larger than the supply of new PhDs emerging from academic institutions.

The purpose of this book is for you to understand the concepts and principles behind ML, with the practical goal of passing the AWS Certified Machine Learning Specialty exam. As practicing ML solution architects, we go well beyond the scope of the test in this book and incorporate architecture patterns and best practices that we have seen employed in the industry today. Reading this book will also give you an understanding of what is required to be a successful machine learning architect.

This is not a book on ML foundations. That is simply too vast a field for us to do it justice in this book and also is not our intention. There are a number of excellent textbooks and online resources you can use to develop a foundation on ML algorithms, deep learning, and similar topics. However, we will cover the concepts that you will need for the test.

Finally, one of our favorite leadership principles here at Amazon that widely applies to the solution architect role is learn and be curious. We have found that the best way to learn a topic is to get hands-on, and we highly recommend that you go beyond this book and get hands-on experience in ML. Download and explore some public datasets, and train some simple predictive models. Build a neural network from scratch using TensorFlow/PyTorch or just native Python. Explore AWS services such as Amazon SageMaker by running some of the sample Jupyter Notebooks. We highly recommend getting some hands-on knowledge before taking the test. Check out the AWS Training and Certification web page for helpful courses: www.aws.training.

Don't just study the questions and answers! The questions on the actual exam will be different from the practice questions included in this book. The exam is designed to test your knowledge of a concept or objective, so use this book to learn the objectives behind the questions.

The ML space is maturing and growing very quickly; what this means is that our book is just a snapshot in time of our understanding of the industry and certification requirements. We highly recommend that you read the SageMaker home page to review the latest releases that may appear on the test.

The AWS Certified Machine Learning Specialty Exam

The AWS Certified Machine Learning Specialty exam is intended for professionals who perform a data science, machine learning engineer role. The official details of the test can be found here: https://aws.amazon.com/certification/certified-machine-learning-specialty.

The focus of the test is to validate your understanding of foundational ML concepts, foundations of statistics, data analysis, exploration, feature engineering, and common ML algorithms. This is required knowledge for anyone performing this role in industry today. However, in addition to this, this certification focuses on your ability to deploy those solutions on AWS and to be able to architect an end-to-end solution on AWS from data ingestion to model deployment and monitoring using a host of relevant AWS services for a given business use case.

Why Become AWS Machine Learning Specialty Certified?

There are several good reasons to get your AWS Certified Machine Learning certification:

• It provides proof of professional achievement.   Certifications are quickly becoming status symbols in the computer service industry. Organizations, including members of the computer service industry, are recognizing the benefits of cloud certification such as the AWS Solution Architect Professional, Certified Security, and Advanced Networking Specialty. As ML becomes increasingly popular, these certifications provide proof of your understanding of ML and your ability to practically deploy ML solutions on AWS.
• It provides an opportunity for advancement.   The solution architect role is one of the most coveted roles in the tech industry today due to the breadth and depth of the knowledge you gain, while having an outsized impact on customers' business. The Machine Learning Specialty Certification could provide you with an opportunity to specialize in ML and become a practicing ML architect, a unique role that many employers are looking to hire.
• It helps you develop an industry understanding of ML.   ML education is rapidly becoming a crowded space with blogs, textbooks, online courses that cover the foundations of ML, statistics and data science, and even ML tooling. However, there is no substitute for experience, and there isn't much material on actual industry use cases with solutions and best practices (with the exception of some fantastic tech blogs published by companies like Uber, Google, Netflix, Lyft, Airbnb, and many others). This book aims to cover some of that gap by providing you with a practical understanding of building real-life ML solutions on AWS.
• It will satisfy your curiosity.   As technologists and technology enthusiasts, we are constantly learning new areas and expanding our knowledge. One of the best and most fulfilling reasons to take this certification is simply to satiate your curiosity to learn how to build ML solutions on AWS.

How to Become AWS Machine Learning Specialty Certified

The AWS Certified Machine Learning Specialty exam is available to anyone and does not require other AWS certifications as prerequisites. It is recommended, however, that you have 1-2 years of experience developing and architecting ML and deep learning workloads on AWS prior to taking the test. Because it is a specialty certification, it also assumes prior foundational understanding of AWS services for storage, networking, security, databases, and so forth; however, these are not tested in detail.

The exam is administered by Pearson VUE and PSI. To register for the test with PSI, you can register online at https://awsavailability.psiexams.com. To register with Pearson VUE, you can register online using https://home.pearsonvue.com/Clients/Amazon-Web-Services.aspx.

Exam policies can change from time to time. We highly recommend that you check both the PSI and Pearson VUE sites for the most up-to-date information when you begin preparing, when you register, and again a few days before your scheduled exam date.

Who Should Buy This Book

Anybody who wants to pass the AWS Certified Machine Learning Specialty exam may benefit from this book. This book is also helpful for business and IT professionals who want to learn how ML is practically used in the industry and pivot their careers toward an ML-centric role such as a data scientist or ML engineer working on AWS. We include a number of practical case studies, industry best practices, and architecture patterns that we have seen used in industry today from our engagements with hundreds of AWS customers. This book is also essential for data scientists, engineers, and other data professionals who are curious about how you can build, train, and deploy models at scale on AWS.

This book assumes some familiarity with ML and with AWS. If you are completely new to machine learning, we recommend that you first learn some basic ML concepts since this book is mainly focused on the practical aspects of building ML solutions. There are several great resources that cover ML foundations, particularly for building statistical models and for deep learning. Two of our favorites are Aurélion Géron's Hands-on Machine Learning with Scikit-learn and TensorFlow (O'Reilly Publishing) and Francois Chollet's Deep Learning with Python (Manning, 2017). There are also several awesome blogs on Medium.com and TowardsDataScience.com. Finally, we also recommend a number of industry blogs from leading tech companies like Uber, Google, Facebook, Amazon, Airbnb, and others on how they deploy large-scale ML solutions to have a holistic understanding of the industry landscape in this space.

As a practical matter, you'll need a laptop or desktop with which to practice and learn in a hands-on way. This book does not cover labs, and there is no substitute for hands-on experience. Go get familiar with AWS ML services such as SageMaker, as well as the AI services, before taking the test. We also recommend that you explore some public datasets, engineer features, and train simple models as well as some deep learning models.

Study Guide Features

This study guide uses a number of common elements to help you prepare. These include the following:

• Summaries   The summary section of each chapter briefly explains the chapter, allowing you to easily understand what it...