AWS-DATASCI | Practical Data Science with Amazon SageMaker
 
You will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs.
 
Course objectives
In this course, you will:
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.
 
Intended audience
This course is intended for:
Developers
Data Scientists
 
Prerequisites
We recommend that attendees of this course have:
Familiarity with Python programming language
Basic understanding of Machine Learning
 
Course outline
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
 
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