Practical Data Science with Amazon SageMaker

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