Description
Prepare for the AWS Machine Learning Engineer exam smarter and faster and get job-ready with this efficient and authoritative resource
In AWS Certified Machine Learning Engineer Study Guide: Associate (MLA-C01) Exam, veteran AWS Practice Director at Trace3—a leading IT consultancy offering AI, data, cloud and cybersecurity solutions for clients across industries—Dario Cabianca delivers a practical and up-to-date roadmap to preparing for the MLA-C01 exam. You'll learn the skills you need to succeed on the exam as well as those you need to hit the ground running at your first AI-related tech job.
You'll learn how to prepare data for machine learning models on Amazon Web Services, build, train, refine models, evaluate model performance, deploy and secure your machine learning applications against bad actors.
Inside the book:
- Complimentary access to the Sybex online test bank, which includes an assessment test, chapter review questions, practice exam, flashcards, and a searchable key term glossary
- Strategies for selecting and justifying an appropriate machine learning approach for specific business problems and identifying the most efficient AWS solutions for those problems
- Practical techniques you can implement immediately in an artificial intelligence and machine learning (AI/ML) development or data science role
Perfect for everyone preparing for the AWS Certified Machine Learning Engineer -- Associate exam, AWS Certified Machine Learning Engineer Study Guide is also an invaluable resource for those preparing for their first role in AI or data science, as well as junior-level practicing professionals seeking to review the fundamentals with a convenient desk reference.
Table of Contents
Acknowledgments ix
About the Author xi
About the Technical Editor xiii
Introduction xv
The AWS Certified Machine Learning Engineer – Associate Exam xv
Who Should Buy This Book xviii
Study Guide Features xviii
AWS Certified Machine Learning Engineer Exam Objectives xix
Assessment Test xxvii
Answers to Assessment Test xxxii
Chapter 1 Introduction to Machine Learning 1
Chapter 2 Data Ingestion and Storage 27
Chapter 3 Data Transformation and Feature Engineering 61
Chapter 4 Model Selection 105
Chapter 5 Model Training and Evaluation 185
Chapter 6 Model Deployment and Orchestration 237
Chapter 7 Model Monitoring and Cost Optimization 297
Chapter 8 Model Security 331
Appendix A Answers to the Review Questions 355
Appendix B Mathematics Essentials 373
Index 391
