Module 1: Introduction to Machine Learning (ML) on AWS Introduction to ML Amazon SageMaker AI Responsible ML

Module 2: Analyzing Machine Learning (ML) Challenges Evaluating ML business challenges ML training approaches ML training algorithms

Module 3: Data Processing for Machine Learning (ML) Data preparation and types Exploratory data analysis AWS storage options and choosing storage

Module 4: Data Transformation and Feature Engineering Handling incorrect, duplicated, and missing data Feature engineering concepts Feature selection techniques 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 Amazon SageMaker AI built-in algorithms Selecting built-in training algorithms Amazon SageMaker Autopilot Model selection considerations ML cost considerations

Module 6: Training Machine Learning (ML) Models Model training concepts Training models in Amazon SageMaker AI Lab 3: Training a model with Amazon SageMaker AI

Module 7: Evaluating and Tuning Machine Learning (ML) models Evaluating model performance Techniques to reduce training time Hyperparameter tuning techniques Lab 4: Model Tuning and Hyperparameter Optimization with Amazon SageMaker AI

Module 8: Model Deployment Strategies Deployment considerations and target options Deployment strategies Choosing a model inference strategy Container and instance types for inference Lab 5: Shifting Traffic A/B