TP3005 Practical Data Science with Amazon SageMaker Training
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.
Objectives
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
Audience
Developers
Data Scientists
Prerequisites
Familiarity with Python programming language
Basic understanding of Machine Learning
Duration
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