Practical Data Science withAmazon SageMaker

Duration: 1 Day (8 Hours)

Practical Data Science withAmazon SageMaker Course Overview:

This course focuses on applying Machine Learning (ML) to solve real-world use cases using Amazon SageMaker. Participants will gain practical knowledge in the stages of the data science process, including analyzing and visualizing datasets, data preparation, and feature engineering.

The course also covers the practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. A real-life use case of customer retention analysis is explored to provide insights for customer loyalty programs.

By completing this course, participants will acquire the skills needed to effectively utilize Amazon SageMaker for ML projects and generate actionable results for real-world scenarios.

Course Level: Intermediate

Intended audience
This course is intended for:

  • Developers
  • Data Scientists

Module 1: Introduction to machine learning
  • Types of ML
  • Job Roles in ML
  • Steps in the ML pipeline
  • Training and test dataset defined
  • Introduction to SageMaker
  • Demonstration: SageMaker console
  • Demonstration: Launching a Jupyter notebook
  • Business challenge: Customer churn
  • Review customer churn dataset
  • Demonstration: Loading and visualizing your dataset
  • Exercise 1: Relating features to target variables
  • Exercise 2: Relationships between attributes
  • Demonstration: Cleaning the data
  • 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
  • Automatic hyper parameter tuning with SageMaker
  • Exercises 6-9: Tuning jobs
  • 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
  • Cost of various error types
  • Demo: Binary classification cutoff
  • Accessing Amazon SageMaker notebooks in a VPC
  • Amazon SageMaker batch transforms
  • Amazon SageMaker Ground Truth
  • Amazon SageMaker Neo

We recommend that attendees of this course have:

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

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  • Convenience
  • Cost-effective
  • Self-paced learning
  • Scalability


  • Interaction and collaboration
  • Networking opportunities
  • Real-time feedback
  • Personal attention


  • Familiar environment
  • Confidentiality
  • Team building
  • Immediate application

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