Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use cases include customer retention analysis to inform customer loyalty programs.


Prepare a dataset for training

Train and evaluate a Machine Learning model

Automatically tune a Machine Learning model

Prepare a Machine Learning model for production

Think critically about Machine Learning model results



Data Scientists


Familiarity with Python programming language

Basic understanding of Machine Learning


One day

Outline for Practical Data Science with Amazon SageMaker Training

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

Demo: SageMaker console

Demo: 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

Demo: Loading and Visualizing your dataset

Exercise 1: Relating features to target variables

Exercise 2: Relationships between attributes

Demo: Cleaning the data

Module 5: Training and Evaluating a Model

Types of Algorithms

XGBoost and SageMaker

Demo 5: Training the data

Exercise 3: Finishing the Estimator definition

Exercise 4: Setting hyperparameters

Exercise 5: Deploying the model

Demo: Hyperparameter tuning with SageMaker

Demo: Evaluating Model Performance

Module 6: Automatically Tune a Model

Automatic hyperparameter 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 Scaling

Demo: Configure and Test Autoscaling

Demo: Check Hyperparameter tuning job

Demo: AWS Autoscaling

Exercise 10-11: Set up AWS Autoscaling

Module 8: Relative Cost of Errors