Introduction

The demand for machine learning (ML) engineers has significantly increased, particularly since 2023, when the introduction of ChatGPT revolutionized the artificial intelligence (AI) landscape. This field has seen a substantial interest and investment, as organizations across various sectors recognize the transformative potential of AI. As ML and AI become progressively more sophisticated, the need for skilled professionals to develop, implement, and maintain these systems has never been greater. To meet this demand, the new AWS Certified Machine Learning Engineer - Associate certification was developed to equip aspiring engineers with the knowledge and skills necessary to excel in this dynamic field.

The AWS Certified Machine Learning Engineer - Associate certification is a testament to the proficiency and expertise required to navigate this ever-evolving field. This certification not only validates an individual's technical skills but also underscores their ability to leverage AWS's extensive suite of ML and AI services to drive innovation. As this technology continues to mature, certified professionals are well-positioned to lead the charge in developing cutting-edge AI solutions.

This study guide adopts a methodical approach by walking you step-by-step through all the phases of the ML lifecycle. The exposition of each topic offers a combination of theoretical knowledge, practical exercises with tested code in Python, and necessary diagrams and plots to visually represent ML models and AI in action.

Throughout this study guide, we will delve into the fascinating world of AWS SageMaker AI (formerly known as Amazon SageMaker) and Amazon Bedrock, exploring their numerous features and functionalities. We will cover the core concepts and practical applications, providing you with the knowledge and tools needed to excel as an AWS Machine Learning Engineer. Whether you are just starting your journey or looking to deepen your expertise, this guide will serve as a comprehensive resource to mastering these platforms and achieving certification.

By obtaining the AWS Certified Machine Learning Engineer - Associate certification, you are not just enhancing your skillset but also contributing to the forefront of technological innovation. Let this study guide be your roadmap to success in this rapidly expanding field.

The AWS Certified Machine Learning Engineer - Associate Exam

The AWS Certified Machine Learning Engineer - Associate Exam is intended to validate the technical skills required to design, build, and operationalize well-architected ML workloads on AWS. The exam covers a wide range of topics, including data preparation, feature engineering, model training, model evaluation, and deployment strategies.

The exam consists of 65 questions and has a duration of 130 minutes. It is available in multiple languages, including English, Japanese, Korean, and Simplified Chinese. The exam costs $150 and can be taken at a Pearson VUE testing center or online as a proctored exam. This certification is valid for 3 years.

Your exam results are presented as a scaled score ranging from 100 to 1,000. To pass, a minimum score of 720 is required. This score reflects your overall performance on the exam and indicates whether you have successfully passed.

The official exam guide is available at https://d1.awsstatic.com/training-and-certification/docs-machine-learning-engineer-associate/AWS-Certified-Machine-Learning-Engineer-Associate_Exam-Guide.pdf.

During the writing of this book, "Amazon SageMaker" was renamed "Amazon SageMaker AI." As a result, the first chapters of this book still use the former name, because at that time this was the correct name in use. In this book, the terms "Amazon SageMaker" and "Amazon SageMaker AI" are used interchangeably to denote the new AWS unified platform for data, analytics, ML, and AI. See https://aws.amazon.com/blogs/aws/introducing-the-next-generation-of-amazon-sagemaker-the-center-for-all-your-data-analytics-and-ai.

Why Become AWS Machine Learning Engineer Certified?

The increasing demand for AWS ML and AI engineers-due to the rapid adoption of ML and AI technologies across industries-has made this a perfect time to pursue the AWS Certified Machine Learning Engineer - Associate certification. Companies are looking for skilled professionals who can harness the power of AWS to build, deploy, and manage ML models efficiently. By earning this certification, you can demonstrate your proficiency in using AWS tools and services to drive impactful ML and AI solutions. This certification not only validates your technical skills but also sets you apart in a competitive job market, making you a valuable asset to potential employers.

One of the key reasons to pursue this certification is the comprehensive knowledge you'll gain about AWS's cutting-edge ML and AI services. While preparing for the exam, you'll master the use of Amazon SageMaker AI, a powerful platform for building, training, deploying and monitoring ML models at scale. You'll also explore the latest additions to Amazon SageMaker AI, which continuously evolves to bring together a broad set of AWS ML, AI, and data analytics services. As a result, you'll become proficient in using Amazon Bedrock, a service that simplifies the deployment of foundation models by offering pretrained models from leading AI companies. However, due to the relatively new nature of Amazon Bedrock, there is a lack of in-depth material available, making this certification even more valuable as it positions you at the forefront of emerging AI technologies.

Amazon SageMaker AI and Amazon Bedrock are designed for seamless integration with numerous AWS services that are required during the phases of the ML lifecycle. Therefore, the study continues with extensive coverage of such services. These include storage services (e.g., Amazon S3, Amazon Elastic File System [EFS], Amazon FSx for Lustre, and others), ingestion services (e.g., Amazon Data Firehose, Amazon Kinesis Data Streams, Amazon Managed Streaming for Apache Kafka [MSK], and others), deployment services (e.g., Amazon Elastic Compute Cloud [EC2], Amazon Elastic Container Service [ECS], and others), orchestration services (e.g., AWS Step Functions, Amazon Managed Workflows for Apache Airflow [MWAA], and others), monitoring, cost optimization, and security services, just to name a few.

Another significant advantage of becoming AWS Machine Learning Engineer certified is the access to exclusive resources and a supportive community of professionals. By joining the certified AWS community, you'll have the opportunity to network with other professionals, share knowledge, and stay updated on the latest trends and advancements in the field. This certification not only boosts your career prospects, but also keeps you engaged in a dynamic and constantly evolving industry.

How to Become AWS Machine Learning Engineer Certified

Your journey to become AWS Machine Learning Engineer Certified begins with a structured approach that covers foundational knowledge, hands-on practice, and thorough exam preparation. This study guide is crafted to mirror that journey.

• Foundational knowledge Start by building a robust understanding of ML concepts, formulate ML problems, and learn algorithms and statistical methods. It's also important to grasp the basics of linear algebra, calculus, probability, and statistics, as they form the mathematical foundation for ML. Additionally, familiarize yourself with AWS services, particularly Amazon SageMaker AI, which provides tools and features for every phase of the ML lifecycle. Learning Python, the primary programming language used in ML, is also essential.
• Hands-on practice Engage in practical experience through AWS resources like tutorials, labs, and workshops. Focus on using Amazon SageMaker AI for various phases of the ML lifecycle, including
  • Data preparation Use Amazon SageMaker Data Wrangler to simplify data preparation and feature engineering.
  • Model building Leverage Amazon SageMaker Studio for an integrated development environment that supports building, training, and debugging ML models.
  • Model training Utilize Amazon SageMaker Training to efficiently train models with built-in algorithms or your own custom code.
  • Model deployment Use Amazon SageMaker Endpoint to deploy trained models for real-time predictions, and Amazon SageMaker Batch Transform for batch predictions.
  • Model monitoring Employ Amazon SageMaker Model Monitor to continuously monitor the performance of deployed models and ensure that they remain accurate over time.
• By working on real-world projects that cover the entire ML lifecycle, you'll gain hands-on experience and deepen your understanding.
• Exam preparation Use AWS's official exam guide to understand key objectives. Utilize practice exams and sample questions to test your readiness. Regular review and practice will ensure that you are well-prepared for the certification exam. On exam day, manage your time effectively and read each question carefully to increase your chances of passing and earning the certification.

Who Should Buy This Book

This book is intended for a broad audience of software, data, and cloud engineers/architects with ideally 1 year of hands-on experience with AWS services. Given the engineering...