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AWS Authorized Training Course - Practical Data Science with Amazon SageMaker

Course Summary

The Data Science with Amazon SageMaker training course is designed to demonstrate how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker.

The course begins by introducing the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Next, it examines the practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. The course concludes by analyzing customer retention patterns to inform customer loyalty programs.

Prerequisites:

    • Familiarity with Python programming language

    • Basic understanding of Machine Learning

AWS Authorized Training is only available in Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Mexico, United States, and Peru.

THIS COURSE IS NOT ELIGIBLE FOR TRAINING BUNDLES.

Purpose
 This course demonstrates how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker
Audience
 for Developers and Data Scientists
Role
Data Scientist - Software Developer
Skill Level
Intermediate
Style
Workshops
Duration
1 Day

 

Productivity Objectives
  • 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

What You'll Learn:

In the AWS Authorized Training Course - Practical Data Science with Amazon SageMaker training course, you'll learn:
  • 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
    • Demonstrate the SageMaker console
    • Demonstrate the Launching a Jupyter notebook
  • Problem formulation and dataset preparation
    • Business challenge: Customer churn
    • Review customer churn dataset
  • Data analysis and visualization
    • Demonstration: Loading and visualizing your dataset
    • Relate features to target variables
    • Relationships between attributes
    • Demonstrate how to clean the data
  • Training and evaluating a model
    • Types of algorithms
    • XGBoost and SageMaker
    • Demonstrate how to train the data
    • Finish the estimator definition
    • Set hyper parameters
    • Deploy the model
    • Demonstrate the hyper parameter tuning with SageMaker
    • Demonstrate the evaluating model performance
  • Automatically tune a model
    • Automatic hyper parameter tuning with SageMaker
    • Exercises 6-9: Tune jobs
  • Deployment/production readiness
    • Deploy a model to an endpoint
    • A/B deployment for testing
    • Auto Scaling
    • Demonstrate how to configure and test auto scaling
    • Demonstrate how to check hyper parameter tuning job
    • Demonstrate the AWS Auto Scaling
    • Set up AWS Auto Scaling
  • Relative cost of errors
    • Cost of various error types
    • Binary classification cutoff
  • Amazon SageMaker architecture and features
    • Access Amazon SageMaker notebooks in a VPC
    • Amazon SageMaker batch transforms
    • Amazon SageMaker Ground Truth
    • Amazon SageMaker Neo
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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