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 3: Problem formulation and dataset preparation
- Business challenge: Customer churn
- Review customer churn dataset
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
