Practical Data Science with SageMaker - 1 Day Instructor-Led Training

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.

Start your AWS Machine Learning journey by accessing Official AWS e-Learning for FREE. Learn What is Machine Learning, AWS Foundations: Machine Learning Basics, The Machine Learning Process and more - GET STARTED 

Level: Intermediate

Duration: 1 Day

Delivery Type: Instructor-Led Training

• 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

Required

• Familiarity with Python programming language
• Basic understanding of Machine Learning

• Developers
• Data Scientists

Day 1

Module 1: Introduction to machine learning

• Types of ML
• Job Roles in ML
• Steps in the ML pipeline

Module 2: Introduction to data prep and SageMaker

• Training and test dataset defined
• Introduction to SageMaker
• Demonstration: SageMaker console
• Demonstration: Launching a Jupyter notebook

Module 3: Problem formulation and dataset preparation

• Business challenge: Customer churn
• Review customer churn dataset

Module 4: Data analysis and visualization

• Demonstration: Loading and visualizing your dataset
• Exercise 1: Relating features to target variables
• Exercise 2: Relationships between attributes
• Demonstration: Cleaning the data

Module 5: Training and evaluating a model

• Types of algorithms
• XGBoost and SageMaker
• Demonstration: Training the data
• Exercise 3: Finishing the estimator definition
• Exercise 4: Setting hyper parameters
• Exercise 5: Deploying the model
• Demonstration: hyper parameter tuning with SageMaker
• Demonstration: Evaluating model performance

Module 6: Automatically tune a model

• Automatic hyper parameter tuning with SageMaker
• Exercises 6-9: Tuning jobs

Module 7: Deployment / production readiness

• Deploying a model to an endpoint
• A/B deployment for testing
• Auto Scaling
• Demonstration: Configure and test auto scaling
• Demonstration: Check hyper parameter tuning job
• Demonstration: AWS Auto Scaling
• Exercise 10-11: Set up AWS Auto Scaling

Module 8: Relative cost of errors

• Cost of various error types
• Demo: Binary classification cutoff

Module 9: Amazon SageMaker architecture and features

• Accessing Amazon SageMaker notebooks in a VPC
• Amazon SageMaker batch transforms
• Amazon SageMaker Ground Truth
• Amazon SageMaker Neo