,Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
Acknowledgments
About the Author
About the Technical Editor
Introduction
Chapter 1: Introduction to Machine Learning
Understanding Artificial Intelligence
Understanding Machine Learning
Understanding Deep Learning
Summary
Exam Essentials
Review Questions
Chapter 2: Data Ingestion and Storage
Introducing Ingestion and Storage
Ingesting and Storing Data
Summary
Exam Essentials
Review Questions
Chapter 3: Data Transformation and Feature Engineering
Introduction
Understanding Feature Engineering
Data Cleaning and Transformation
, Feature Engineering Techniques
Data Labeling
Managing Class Imbalance
Data Splitting
Summary
Exam Essentials
Review Questions
Chapter 4: Model Selection
Understanding AWS AI Services
Developing Models with Amazon SageMaker Built-in
Algorithms
Criteria for Model Selection
Summary
Exam Essentials
Review Questions
Chapter 5: Model Training and Evaluation
Training
Hyperparameter Tuning
Model Performance Evaluation
Deep-Dive Model Tuning Example
Summary
Exam Essentials
Review Questions
Chapter 6: Model Deployment and Orchestration
AWS Model Deployment Services
Advanced Model Deployment Techniques
Orchestrating ML Workflows
Deep Dive Model Deployment Example
Summary
, Exam Essentials
Review Questions
Chapter 7: Model Monitoring and Cost Optimization
Monitoring Model Inference
Monitoring Infrastructure and Cost
Summary
Exam Essentials
Review Questions
Chapter 8: Model Security
Security Design Principles
Securing AWS Services
Summary
Exam Essentials
Review Questions
Appendix A: Answers to the Review Questions
Chapter 1: Introduction to Machine Learning
Chapter 2: Data Ingestion and Storage
Chapter 3: Data Transformation and Feature Engineering
Chapter 4: Model Selection
Chapter 5: Model Training and Evaluation
Chapter 6: Model Deployment and Orchestration
Chapter 7: Model Monitoring and Cost Optimization
Chapter 8: Model Security
Appendix B: Mathematics Essentials
Linear Algebra
Statistics
Probability Theory
Calculus
Index
Cover
Table of Contents
Title Page
Copyright
Dedication
Acknowledgments
About the Author
About the Technical Editor
Introduction
Chapter 1: Introduction to Machine Learning
Understanding Artificial Intelligence
Understanding Machine Learning
Understanding Deep Learning
Summary
Exam Essentials
Review Questions
Chapter 2: Data Ingestion and Storage
Introducing Ingestion and Storage
Ingesting and Storing Data
Summary
Exam Essentials
Review Questions
Chapter 3: Data Transformation and Feature Engineering
Introduction
Understanding Feature Engineering
Data Cleaning and Transformation
, Feature Engineering Techniques
Data Labeling
Managing Class Imbalance
Data Splitting
Summary
Exam Essentials
Review Questions
Chapter 4: Model Selection
Understanding AWS AI Services
Developing Models with Amazon SageMaker Built-in
Algorithms
Criteria for Model Selection
Summary
Exam Essentials
Review Questions
Chapter 5: Model Training and Evaluation
Training
Hyperparameter Tuning
Model Performance Evaluation
Deep-Dive Model Tuning Example
Summary
Exam Essentials
Review Questions
Chapter 6: Model Deployment and Orchestration
AWS Model Deployment Services
Advanced Model Deployment Techniques
Orchestrating ML Workflows
Deep Dive Model Deployment Example
Summary
, Exam Essentials
Review Questions
Chapter 7: Model Monitoring and Cost Optimization
Monitoring Model Inference
Monitoring Infrastructure and Cost
Summary
Exam Essentials
Review Questions
Chapter 8: Model Security
Security Design Principles
Securing AWS Services
Summary
Exam Essentials
Review Questions
Appendix A: Answers to the Review Questions
Chapter 1: Introduction to Machine Learning
Chapter 2: Data Ingestion and Storage
Chapter 3: Data Transformation and Feature Engineering
Chapter 4: Model Selection
Chapter 5: Model Training and Evaluation
Chapter 6: Model Deployment and Orchestration
Chapter 7: Model Monitoring and Cost Optimization
Chapter 8: Model Security
Appendix B: Mathematics Essentials
Linear Algebra
Statistics
Probability Theory
Calculus
Index
