AWS Certified ML Specialty Guide

Navigating the AWS Certified Machine Learning - Specialty exam from novice to expert (English Edition)

Arun Arunachalam

• Publisher: BPB Publications
• Year: 2025
• eBook ISBN: 9789365896428
• Topic: Computer Science
• Subtopic: Certification Guides in Computer Science
• Index: Computer Science

Description
Amazon Web Services is the world's most comprehensive and broadly adopted cloud computing platform, providing on-demand access to IT resources, such as computing power, database storage, and other essential services, over the internet with pay-as-you-go pricing. With its vast array of services and tools, AWS provides a scalable and flexible environment for developing, deploying, and managing ML models.

The purpose of the book is to empower individuals with basic AWS Cloud knowledge to leverage this advanced technology and obtain the coveted AWS Certified Machine Learning - Specialty certification. By mastering the intricacies of AWS ML services, readers can unlock new career opportunities and contribute to the ever-evolving field of ML. It guides the readers through the domains of data engineering, exploratory data analysis, modeling, and ML implementation and operations. Covering key concepts and practices, this guide equips individuals with fundamental AWS Cloud knowledge.

By the end of this book, readers will learn to create efficient data repositories, perform data transformation, sanitize and prepare data, engineer features, select and train ML models, optimize performance, build scalable solutions, leverage AWS ML services, apply security practices, and deploy operational ML solutions.

What you will learn
? Understanding AWS ML services, including SageMaker, Lambda, Glue, and other ML tools.
? Design secure S3, EFS, and EBS repositories, implement data ingestion solutions, and perform data transformation.
? Frame business problems; select supervised, unsupervised, or ensemble models.
? Sanitize and prepare data for modeling, perform feature engineering, and analyze data for ML.
? Solving ML problems by selecting and training appropriate ML models.
? Perform hyperparameter optimization, evaluate ML models, and build performant ML solutions.
? Deploy models, set A/B testing, IAM security, and auto-scaling pipelines.
? Apply AWS security practices to ML solutions and deploy operational ML systems.

Who this book is for
This book is designed for aspiring ML specialists, data scientists, data engineers, cloud architects, and any professionals seeking to enhance their skills and knowledge in AWS ML services. Readers should possess a basic understanding of ML concepts, experience with a programming language like Python, and foundational familiarity with core AWS services.

Table of Contents
1. Creating Data Repositories for Machine Learning
2. Implementing Data Ingestion Solutions
3. Transforming Data into Insights
4. Data Sanitization and Preparation
5. Feature Engineering
6. Data Analysis and Visualization
7. Framing Business Problems as ML Problems
8. Selecting Appropriate ML Models
9. Training ML Models
10. Hyperparameter Optimization
11. Evaluating ML Models
12. Building ML Solutions for Performance and Scalability
13. Recommending and Implementing Appropriate ML Services
14. Applying AWS Security Practices to ML Solutions
15. Deploying and Operationalizing ML Solutions

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication
• About the Author
• About the Reviewers
• Acknowledgement
• Preface
• Table of Contents
• 1. Creating Data Repositories for Machine Learning
• 2. Implementing Data Ingestion Solutions
• 3. Transforming Data into Insights
• 4. Data Sanitization and Preparation
• 5. Feature Engineering
• 6. Data Analysis and Visualization
• 7. Framing Business Problems as ML Problems
• 8. Selecting Appropriate ML Models
• 9. Training ML Models
• 10. Hyperparameter Optimization
• 11. Evaluating ML Models
• 12. Building ML Solutions for Performance and Scalability
• 13. Recommending and Implementing Appropriate ML Services
• 14. Applying AWS Security Practices to ML Solutions
• 15. Deploying and Operationalizing ML Solutions
• Appendix
• Index