Practical Data Science with Amazon SageMaker

Learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker.

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.

1 day/8 hours of instruction
Public Classroom Pricing

Starting at: $675(USD)

Group Rate: $625

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

Download the Course Brochure

Part 1: Introduction to Machine Learning

  1. Types of ML
  2. Job Roles in ML
  3. Steps in the ML pipeline

Part 2: Introduction to Data Prep and SageMaker

  1. Training and Test dataset defined
  2. Introduction to SageMaker
  3. Demo: SageMaker console
  4. Demo: Launching a Jupyter notebook

Part 3: Problem formulation and Dataset Preparation

  1. Business Challenge: Customer churn
  2. Review Customer churn dataset

Part 4: Data Analysis and Visualization

  1. Demo: Loading and Visualizing your dataset
  2. Exercise 1: Relating features to target variables
  3. Exercise 2: Relationships between attributes
  4. Demo: Cleaning the data

Part 5: Training and Evaluating a Model

  1. Types of Algorithms
  2. XGBoost and SageMaker
  3. Demo 5: Training the data
  4. Exercise 3: Finishing the Estimator definition
  5. Exercise 4: Setting hyperparameters
  6. Exercise 5: Deploying the model
  7. Demo: Hyperparameter tuning with SageMaker
  8. Demo: Evaluating Model Performance

Part 6: Automatically Tune a Model

  1. Automatic hyperparameter tuning with SageMaker
  2. Exercises 6-9: Tuning Jobs

Part 7: Deployment / Production Readiness

  1. Deploying a model to an endpoint
  2. A/B deployment for testing
  3. Auto Scaling Scaling
  4. Demo: Configure and Test Autoscaling
  5. Demo: Check Hyperparameter tuning job
  6. Demo: AWS Autoscaling
  7. Exercise 10-11: Set up AWS Autoscaling

Part 8: Relative Cost of Errors

  • Developers
  • Data Scientists

  • Prepare a dataset for training
  • Train and evaluate a Machine Learning mode
  • Automatically tune a Machine Learning model
  • Prepare a Machine Learning model for production
  • Think critically about Machine Learning model results

A full refund will be issued for class cancellations made at least 10 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 10 business days from the course start date and for No‑Shows (students who do not attend class).
For reschedules made within 10 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of six months from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.

Practical Data Science with Amazon SageMaker Schedule

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Live OnlineUSA Based

Nov 18th - 18th
9:00 AM - 5:00 PM ET

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