Data Science with Amazon SageMaker Course

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.

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

Download

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

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about our training

Real-World Content

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

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

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