Practical Data Science with Amazon SageMaker (MLPDSS)

In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.


Audience

This course is intended for:

  • Developers
  • Data Scientists


Prerequisites

  • Familiarity with Python programming language
  • Basic understanding of Machine Learning


Objectives

In this course, you will learn how to:

  • 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


Course Content

Introduction to Machine Learning

  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline


Introduction to Data Prep and SageMaker

  • Training and Test dataset defined
  • Introduction to SageMaker
  • Demo: SageMaker console
  • Demo: Launching a Jupyter notebook


Problem formulation and Dataset Preparation

  • Business Challenge: Customer churn
  • Review Customer churn dataset


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


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


Automatically Tune a Model

  • Automatic hyperparameter tuning with SageMaker
  • Exercises 6-9: Tuning Jobs


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
  • Cost of various error types
  • Demo: Binary Classification cutoff


Amazon SageMaker Architecture and features

  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo