1 day


Learn how to solve a real-world use case with machine learning 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.

Please note: students are required to bring their own laptop/tablet and power cable for this course.

Key Topics

Detailed Info
  • Introduction to Machine Learning
  • Problem Formulation and Dataset Preparation
  • Training and Evaluating a Model
  • Deployment / Production Readines
  • Introduction to Data Prep and SageMaker
  • Data Analysis and Visualization
  • Automatically Tune a Model
  • Relative Cost of Errors
  • Amazon SageMaker Architecture and Features
Skills Gained
Key Topics
Target Audience

Skills Gained

In this course, participants 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

Key Topics

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

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Target Audience

This course is intended for:

  • Developers 
  • Data Scientists


We recommend that attendees of this course have the following prerequisites:

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

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected]

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The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.

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