AWS Certified Machine Learning Engineer - Associate

Introduction

AWS Certified Machine Learning Engineer - Associate: Introduction

Module 1: Data Preparation for Machine Learning

Module Introduction

Lesson 1: Data Ingestion and Storage Basics

Learning objectives
1.1 Course Overview
1.2 Overview of Common Data Formats
1.3 Ingesting Data with Amazon S3 and SageMaker Data Wrangler
1.4 Data Ingestion Demonstration
1.5 Data Storage Optimization and Transfer in AWS

Lesson 2: Data Transformation and Feature Engineering with SageMaker

Learning objectives
2.1 Data Cleaning and Preprocessing with SageMaker Data Wrangler
2.2 Data Preprocessing Demonstration
2.3 Feature Scaling and Encoding Techniques
2.4 Handling Missing Values and Outliers

Lesson 3: Preparing Data for Modeling

Learning objectives
3.1 Validating Data Quality with AWS Tools
3.2 Configuring Data for SageMaker Training Jobs
3.3 Managing SageMaker Feature Store
3.4 SageMaker Feature Store Demonstration

Module 2: ML Model Development

Module Introduction

Lesson 4: Choosing and Training Models in SageMaker

Learning objectives
4.1 Overview of SageMaker's Built-in Algorithms and JumpStart Models
4.2 SageMaker Algorithms Demonstration
4.3 Setting Up and Running SageMaker Training Jobs
4.4 SageMaker Training Demonstration
4.5 Hyperparameter Tuning with SageMaker Automatic Model Tuning
4.6 Hyperparameter Tuning Demonstration
4.7 Preventing Overfitting and Underfitting
4.8 Model Over/Underfitting Demonstration

Lesson 5: Model Evaluation and Bias Detection

Learning objectives
5.1 Model Evaluation Metrics: Accuracy, Precision, and Recall
5.2 Using SageMaker Clarify for Bias Detection and Interpretability
5.3 Comparing Model Performance Using A/B Testing
5.4 Model A/B Testing Demonstration
5.5 Managing Model Versions with SageMaker Model Registry
5.6 Model Registry Demonstration

Module 3: Deployment and Orchestration of ML Workflows

Module Introduction

Lesson 6: Deploying Models with SageMaker

Learning objectives
6.1 Real-Time Inference with SageMaker Endpoints
6.2 Real-Time Inference Demonstration
6.3 Batch Inference and Asynchronous Inference
6.4 Batch and Asynchronous Inference Demonstration
6.5 Using SageMaker Neo for Edge Deployment
6.6 SageMaker Edge Deployment Demonstration

Lesson 7: Automating ML Workflows with SageMaker Pipelines

Learning objectives
7.1 Building and Automating ML Pipelines in SageMaker
7.2 SageMaker Pipeline Demonstration
7.3 Integrating Data Processing and Training Steps
7.4 Training and Data Processing in SageMaker Pipelines Demonstration
7.5 Triggering Pipelines with EventBridge for Retraining
7.6 Triggering SageMaker Pipelines via EventBridge Demonstration

Module 4: ML Solution Monitoring, Maintenance, and Security

Module Introduction

Lesson 8: Monitoring and Optimizing ML Solutions

Learning objectives
8.1 Using SageMaker Model Monitor for Data Drift and Quality
8.2 SageMaker Model Monitor Demonstration
8.3 Setting Up Alerts and CloudWatch Dashboards
8.4 Cost Optimization with Auto-scaling and SageMaker Savings Plans
8.5 SageMaker Auto-scaling Demonstration

Lesson 9: Securing ML Models and Data in SageMaker

Learning objectives
9.1 IAM Roles and Permissions for SageMaker
9.2 IAM Demonstration
9.3 VPC Configurations for Secure Endpoint Deployment
9.4 VPC Demonstration

Module 5: Exam Preparation and Strategy

Module Introduction

Lesson 10: Exam Strategies and Practice

Learning objectives
10.1 Key Focus Areas for the AWS ML Engineer Associate Exam
10.2 Exam Question Types and Tips for Multiple-Choice Questions
10.3 Time Management and Final Exam Day Tips

Summary

AWS Certified Machine Learning Engineer - Associate: Summary