In this intermediate-level course, individuals 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 cases include customer retention analysis to inform customer loyalty programs.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

By the end of this course, you will be able 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

  • 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
    Relative Cost of Errors


Developers and Data Scientists will benefit from this course.




Familiarity with Python programming language Basic understanding of Machine Learning


Length: 1.0 day (8 hours)


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