Machine Learning Engineering on AWS

Module 0: Course Introduction

Module 1: Introduction to Machine Learning (ML) on AWS

• Topic A: Introduction to ML
• Topic B: Amazon SageMaker AI
• Topic C: Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges

• Topic A: Evaluating ML business challenges
• Topic B: ML training approaches
• Topic C: ML training algorithms

Module 3: Data Processing for Machine Learning (ML)

• Topic A: Data preparation and types
• Topic B: Exploratory data analysis
• Topic C: AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering

• Topic A: Handling incorrect, duplicated, and missing data
• Topic B: Feature engineering concepts
• Topic C: Feature selection techniques
• Topic D: AWS data transformation services
• Lab 1: Analyze and Prepare Data with Amazon SageMaker Data Wrangler and Amazon EMR
• Lab 2: Data Processing Using SageMaker Processing and the SageMaker Python SDK

Module 5: Choosing a Modeling Approach

• Topic A: Amazon SageMaker AI built-in algorithms
• Topic B: Selecting built-in training algorithms
• Topic C: Amazon SageMaker Autopilot
• Topic D: Model selection considerations
• Topic E: ML cost considerations

Module 6: Training Machine Learning (ML) Models

• Topic A: Model training concepts
• Topic B: Training models in Amazon SageMaker AI
• Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models

• Topic A: Evaluating model performance
• Topic B: Techniques to reduce training time
• Topic C: Hyperparameter tuning techniques
• Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies

• Topic A: Deployment considerations and target options
• Topic B: Deployment strategies
• Topic C: Choosing a model inference strategy
• Topic D: Container and instance types for inference
• Lab 5: Shifting Traffic A/B

Module 9: Securing AWS Machine Learning (ML) Resources

• Topic A: Access control
• Topic B: Network access controls for ML resources
• Topic C: Security considerations for CI/CD pipelines

Module 10: Machine Learning Operations (MLOps) and Automated Deployment

• Topic A: Introduction to MLOps
• Topic B: Automating testing in CI/CD pipelines
• Topic C: Continuous delivery services
• Lab 6: Using Amazon SageMaker Pipelines and the Amazon SageMaker Model Registry with Amazon SageMaker Studio

Module 11: Monitoring Model Performance and Data Quality

• Topic A: Detecting drift in ML models
• Topic B: SageMaker Model Monitor
• Topic C: Monitoring for data quality and model quality
• Topic D: Automated remediation and troubleshooting
• Lab 7: Monitoring a Model for Data Drift

Module 12: Course Wrap-up