Module 1: Introduction to machine learning Types of ML Job Roles in ML Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker Training and test dataset defined Introduction to SageMaker Demonstration: SageMaker console Demonstration: Launching a Jupyter notebook

Module 4: Data analysis and visualization Demonstration: Loading and visualizing your dataset Exercise 1: Relating features to target variables Exercise 2: Relationships between attributes Demonstration: Cleaning the data

Module 5: Training and evaluating a model Types of algorithms XGBoost and SageMaker Demonstration: Training the data Exercise 3: Finishing the estimator definition Exercise 4: Setting hyper parameters Exercise 5: Deploying the model Demonstration: hyper parameter tuning with SageMaker Demonstration: Evaluating model performance

Module 6: Automatically tune a model Automatic hyper parameter tuning with SageMaker Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness Deploying a model to an endpoint A/B deployment for testing Auto Scaling Demonstration: Configure and test auto scaling Demonstration: Check hyper parameter tuning job Demonstration: AWS Auto Scaling Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors Cost of various error types Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features Accessing Amazon SageMaker notebooks in a VPC Amazon SageMaker batch transforms Amazon SageMaker Ground Truth Amazon SageMaker Neo