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AWS Certified Machine Learning Engineer Study Guide

Master AWS ML engineering. This guide covers data to deployment, ensuring you build, train, and deploy robust models.

(AWS-MLA.AE1) / ISBN : 979-8-90059-121-6
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About This Course

his isn't a theoretical overview; it's a deep dive into becoming an AWS Certified Machine Learning Engineer. We'll dissect the entire ML lifecycle on AWS, from raw data ingestion using services like S3 and Kinesis to advanced model deployment with SageMaker. Expect to grapple with real-world data transformation challenges, understand why certain feature engineering techniques fail, and learn to optimize model performance under strict budget constraints. We'll cover critical aspects like hyperparameter tuning, model monitoring, and securing your ML pipelines. You'll gain practical skills to avoid common pitfalls, ensuring your models don't just work, but perform reliably and cost-effectively in production. This course prepares you for the exam and the job.

Skills You’ll Get

  • Architecting and implementing robust data ingestion and storage solutions on AWS for diverse ML workloads, understanding the trade-offs between latency and cost.
  • Applying advanced feature engineering and data transformation techniques to raw datasets, recognizing how data quality directly impacts model accuracy and deployment viability.
  • Developing, training, and evaluating machine learning models using Amazon SageMaker, including hyperparameter tuning and identifying common overfitting/underfitting failure points.
  • Deploying, orchestrating, and monitoring ML models in production environments on AWS, ensuring security, cost-efficiency, and operational resilience against real-world data drift.
Download Course Outline

1

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
2

Introduction to Machine Learning

  • Understanding Artificial Intelligence
  • Understanding Machine Learning
  • Understanding Deep Learning
  • Summary
  • Exam Essentials
3

Data Ingestion and Storage

  • Introducing Ingestion and Storage
  • Ingesting and Storing Data
  • Summary
  • Exam Essentials
4

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
5

Model Selection

  • Understanding AWS AI Services
  • Developing Models with Amazon SageMaker Built-in Algorithms
  • Criteria for Model Selection
  • Summary
  • Exam Essentials
6

Model Training and Evaluation

  • Training
  • Hyperparameter Tuning
  • Model Performance Evaluation
  • Deep-Dive Model Tuning Example
  • Summary
  • Exam Essentials
7

Model Deployment and Orchestration

  • AWS Model Deployment Services
  • Advanced Model Deployment Techniques
  • Orchestrating ML Workflows
  • Deep Dive Model Deployment Example
  • Summary
  • Exam Essentials
8

Model Monitoring and Cost Optimization

  • Monitoring Model Inference
  • Monitoring Infrastructure and Cost
  • Summary
  • Exam Essentials
9

Model Security

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

Appendix B: Mathematics Essentials

  • Linear Algebra
  • Statistics
  • Probability Theory
  • Calculus
11

Flashcards

12

Practice Exam 

1

Introduction to Machine Learning

  • Rebuilding Clarity Through Broken AI Decisions and Model Choices
2

Data Ingestion and Storage

  • Creating a S3 Glacier Storage Using Lifecycle Rules
  • Creating ETL Resources Using AWS Glue
  • Creating an Amazon DynamoDB Table
3

Data Transformation and Feature Engineering

  • Detecting Objects in an Image Using Amazon Rekognition
4

Model Selection

  • Using Amazon Lex to Build a Chatbot
5

Model Deployment and Orchestration

  • Creating an AWS Lambda Function
  • Generating AI Responses Using Amazon Bedrock Playground
  • Launching an EC2 Instance
6

Model Monitoring and Cost Optimization

  • Creating Resources with AWS CloudFormation
  • Implementing AWS CloudTrail for Security Monitoring
  • Analyzing Security Logs in AWS Lambda Using CloudWatch
  • Creating a Rule in Amazon EventBridge
  • Detecting Threats with AWS GuardDuty
7

Model Security

  • Creating an AWS WAF Web ACL
  • Creating an NACL
  • Creating a Security Group
  • Creating an IAM User
  • Restricting S3 Access via a VPC Endpoint Policy
  • Creating and Managing IAM Policies

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While it covers foundational ML concepts, this guide assumes a basic understanding of AWS services and Python. We'll build from there, but if you're entirely new to cloud or programming, expect a steeper learning curve.

You'll engage with 20 hands-on labs and 132 practice exercises. This isn't just theory; you'll be configuring services, writing code, and deploying models, which is crucial for understanding real-world limitations.You'll engage with 20 hands-on labs and 132 practice exercises. This isn't just theory; you'll be configuring services, writing code, and deploying models, which is crucial for understanding real-world limitations.

Beyond comprehensive content, you get 90 practice quizzes, 101 flashcards, and a full practice exam. We focus on the exam objectives, but more importantly, on the practical knowledge needed to answer scenario-based questions effectively.

We explicitly cover issues like model drift, managing inference costs, securing endpoints, and orchestrating complex pipelines. Expect to learn how to monitor for these failures and implement resilient solutions, not just deploy a model once.

The course includes a 'Mathematics Essentials' appendix covering linear algebra, statistics, probability, and calculus. While you don't need to be a mathematician, a solid grasp of these fundamentals is critical for truly understanding model behavior and limitations.

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