AWS Certified Machine Learning Engineer Study Guide

Introduction

• The AWS Certified Machine Learning Engineer – Associate Exam
• Who Should Buy This Course
• Conventions Used in This Course
• Course Objectives
• AWS Certified Machine Learning Engineer Exam Objectives
• Domain 1: Data Preparation for Machine Learning (ML)
• Domain 2: ML Model Development
• Domain 3: Deployment and Orchestration of ML Workflows
• Domain 4: ML Solution Monitoring, Maintenance, and Security

Introduction to Machine Learning

• Understanding Artificial Intelligence
• Understanding Machine Learning
• Understanding Deep Learning
• Summary
• Exam Essentials

Data Ingestion and Storage

• Introducing Ingestion and Storage
• Ingesting and Storing Data
• Summary
• Exam Essentials

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

Model Selection

• Understanding AWS AI Services
• Developing Models with Amazon SageMaker Built-in Algorithms
• Criteria for Model Selection
• Summary
• Exam Essentials

Model Training and Evaluation

• Training
• Hyperparameter Tuning
• Model Performance Evaluation
• Deep-Dive Model Tuning Example
• Summary
• Exam Essentials

Model Deployment and Orchestration

• AWS Model Deployment Services
• Advanced Model Deployment Techniques
• Orchestrating ML Workflows
• Deep Dive Model Deployment Example
• Summary
• Exam Essentials

Model Monitoring and Cost Optimization

• Monitoring Model Inference
• Monitoring Infrastructure and Cost
• Summary
• Exam Essentials

Model Security

• Security Design Principles
• Securing AWS Services
• Summary
• Exam Essentials

Appendix B: Mathematics Essentials

• Linear Algebra
• Statistics
• Probability Theory
• Calculus

Flashcards

Practice Exam