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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.

Available formats for this course
Live Online
Private Team Training
1 day/8 hours of instruction
Public Classroom Pricing

Starting at: $675(USD)


Group Rate: $675

Get the full details on this course. Download the .PDF Brochure below:

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

Demo: SageMaker console
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

Demo: Loading and Visualizing your dataset
Exercise 1: Relating features to target variables
Exercise 2: Relationships between attributes

Demo: Cleaning the data

Part 5: Training and Evaluating a Model

  1. Types of Algorithms
  2. 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

Part 6: Automatically Tune a Model

  1. Automatic hyperparameter tuning with SageMaker

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

Demo: Configure and Test Autoscaling
Demo: Check Hyperparameter tuning job
Demo: AWS Autoscaling
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

There are currently no scheduled classes for this course. Please contact us if you would like more information or to schedule this course for you or your company.

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