Practical Data Science with Amazon SageMaker Training

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 Details


1 day


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

Target Audience

  • Developers
  • Data Scientists

Skills Gained

  • 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 Outline
  • 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
  • Problem formulation and Dataset Preparation
    • Business Challenge: Customer churn
    • Review Customer churn dataset
  • Data Analysis and Visualization
  • Training and Evaluating a Model
    • Types of Algorithms
    • XGBoost and SageMaker
  • Automatically Tune a Model
    • Automatic hyperparameter tuning with SageMaker
  • Deployment / Production Readiness
    • Deploying a model to an endpoint
    • A/B deployment for testing
    • Auto Scaling Scaling
  • Relative Cost of Errors
    • Cost of various error types
  • Amazon SageMaker architecture and features
    • Accessing Amazon SageMaker notebooks in a VPC
    • Amazon SageMaker batch transforms
    • Amazon SageMaker Ground Truth
    • Amazon SageMaker Neo
Upcoming Course Dates
USD $745
Online Virtual Class
Date: Jun 21, 2024
Time: 12 PM - 8 PM ET
Partner Registration

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