AWS Certified Machine Learning Study Guide

AWS Certified Machine Learning Study Guide

by Shreyas Subramanian, Stefan Natu
Released December 2021
Publisher(s): Sybex
ISBN: 9781119821007

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Book description

Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide 

As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. 

The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. 

From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. 

You’ll also find: 

  • An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud 
  • Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science 
  • Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms 

AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning. 

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Acknowledgments
  6. About the Authors
  7. About the Technical Editor
  8. Introduction
    1. The AWS Certified Machine Learning Specialty Exam
    2. Who Should Buy This Book
    3. Study Guide Features
    4. AWS Certified Machine Learning Specialty Exam Objectives
  9. Assessment Test
  10. Answers to Assessment Test
  11. PART I: Introduction
    1. Chapter 1: AWS AI ML Stack
      1. Amazon Rekognition
      2. Amazon Textract
      3. Amazon Transcribe
      4. Amazon Translate
      5. Amazon Polly
      6. Amazon Lex
      7. Amazon Kendra
      8. Amazon Personalize
      9. Amazon Forecast
      10. Amazon Comprehend
      11. Amazon CodeGuru
      12. Amazon Augmented AI
      13. Amazon SageMaker
      14. AWS Machine Learning Devices
      15. Summary
      16. Exam Essentials
      17. Review Questions
    2. Chapter 2: Supporting Services from the AWS Stack
      1. Storage
      2. Amazon VPC
      3. AWS Lambda
      4. AWS Step Functions
      5. AWS RoboMaker
      6. Summary
      7. Exam Essentials
      8. Review Questions
  12. PART II: Phases of Machine Learning Workloads
    1. Chapter 3: Business Understanding
      1. Phases of ML Workloads
      2. Business Problem Identification
      3. Summary
      4. Exam Essentials
      5. Review Questions
    2. Chapter 4: Framing a Machine Learning Problem
      1. ML Problem Framing
      2. Recommended Practices
      3. Summary
      4. Exam Essentials
      5. Review Questions
    3. Chapter 5: Data Collection
      1. Basic Data Concepts
      2. Data Repositories
      3. Data Migration to AWS
      4. Summary
      5. Exam Essentials
      6. Review Questions
    4. Chapter 6: Data Preparation
      1. Data Preparation Tools
      2. Summary
      3. Exam Essentials
      4. Review Questions
    5. Chapter 7: Feature Engineering
      1. Feature Engineering Concepts
      2. Feature Engineering Tools on AWS
      3. Summary
      4. Exam Essentials
      5. Review Questions
    6. Chapter 8: Model Training
      1. Common ML Algorithms
      2. Local Training and Testing
      3. Remote Training
      4. Distributed Training
      5. Monitoring Training Jobs
      6. Debugging Training Jobs
      7. Hyperparameter Optimization
      8. Summary
      9. Exam Essentials
      10. Review Questions
    7. Chapter 9: Model Evaluation
      1. Experiment Management
      2. Metrics and Visualization
      3. Summary
      4. Exam Essentials
      5. Review Questions
    8. Chapter 10: Model Deployment and Inference
      1. Deployment for AI Services
      2. Deployment for Amazon SageMaker
      3. Advanced Deployment Topics
      4. Summary
      5. Exam Essentials
      6. Review Questions
    9. Chapter 11: Application Integration
      1. Integration with On-Premises Systems
      2. Integration with Cloud Systems
      3. Integration with Front-End Systems
      4. Summary
      5. Exam Essentials
      6. Review Questions
  13. PART III: Machine Learning Well-Architected Lens
    1. Chapter 12: Operational Excellence Pillar for ML
      1. Operational Excellence on AWS
      2. Summary
      3. Exam Essentials
      4. Review Questions
    2. Chapter 13: Security Pillar
      1. Security and AWS
      2. Secure SageMaker Environments
      3. AI Services Security
      4. Summary
      5. Exam Essentials
      6. Review Questions
    3. Chapter 14: Reliability Pillar
      1. Reliability on AWS
      2. Change Management for ML
      3. Failure Management for ML
      4. Summary
      5. Exam Essentials
      6. Review Questions
    4. Chapter 15: Performance Efficiency Pillar for ML
      1. Performance Efficiency for ML on AWS
      2. Summary
      3. Exam Essentials
      4. Review Questions
    5. Chapter 16: Cost Optimization Pillar for ML
      1. Common Design Principles
      2. Cost Optimization for ML Workloads
      3. Summary
      4. Exam Essentials
      5. Review Questions
    6. Chapter 17: Recent Updates in the AWS AI/ML Stack
      1. New Services and Features Related to AI Services
      2. New Features Related to Amazon SageMaker
      3. Summary
      4. Exam Essentials
  14. Appendix Answers to the Review Questions
    1. Chapter 1: AWS AI ML Stack
    2. Chapter 2: Supporting Services from the AWS Stack
    3. Chapter 3: Business Understanding
    4. Chapter 4: Framing a Machine Learning Problem
    5. Chapter 5: Data Collection
    6. Chapter 6: Data Preparation
    7. Chapter 7: Feature Engineering
    8. Chapter 8: Model Training
    9. Chapter 9: Model Evaluation
    10. Chapter 10: Model Deployment and Inference
    11. Chapter 11: Application Integration
    12. Chapter 12: Operational Excellence Pillar for ML
    13. Chapter 13: Security Pillar
    14. Chapter 14: Reliability Pillar
    15. Chapter 15: Performance Efficiency Pillar for ML
    16. Chapter 16: Cost Optimization Pillar for ML
  15. Index
  16. End User License Agreement

Product information

  • Title: AWS Certified Machine Learning Study Guide
  • Author(s): Shreyas Subramanian, Stefan Natu
  • Release date: December 2021
  • Publisher(s): Sybex
  • ISBN: 9781119821007