Practical Data Science with Amazon SageMaker


Course Number: AWS-134

Duration: 1 day (6.5 hours)

Format: Live, hands-on

Amazon SageMaker Training Overview

This live, online or on-site Data Science with Amazon SageMaker training course teaches attendees how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. Participants are guided through the stages of a typical data science process for ML from analyzing and visualizing a dataset to preparing the data. Students also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

All attendees will learn how 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

Prerequisites

All attendees must have taken Accelebrate's AWS Technical Essentials course, or have equivalent knowledge. Students must also have basic knowledge of:

  • The Python Programming language
  • The Jupyter Notebook environment
  • AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)

Outline

Expand All | Collapse All

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
  • Loading and Visualizing your dataset
  • Relating features to target variables
  • Relationships between attributes
  • Cleaning the data
Training and Evaluating a Model
  • Types of Algorithms
  • XGBoost and SageMaker
  • Training the data
  • Finishing the Estimator definition
  • Setting hyperparameters
  • Deploying the model
  • Hyperparameter tuning with SageMaker
  • Evaluating Model Performance
 Automatically Tune a Model
  • Automatic hyperparameter tuning with SageMaker
  • Tuning Jobs
Deployment / Production Readiness
  • Deploying a model to an endpoint
  • A/B deployment for testing
  • Auto Scaling Scaling
  • Configure and Test Autoscaling
  • Check Hyperparameter tuning job
  • AWS Autoscaling
  • Set up AWS Autoscaling
Relative Cost of Errors
Conclusion

Training Materials:

All Amazon SageMaker training students will receive comprehensive courseware.

Software Requirements:

A modern web browser and an Internet connection free of restrictive firewalls, so that the student can connect by SSH or Remote Desktop (RDP) into AWS virtual machines.



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Multiple Payment Options

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