When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Practical Data Science with Amazon SageMaker
You will 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...
Read MoreUpcoming Course Dates
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Jun 21, 2024 - Jun 21, 2024 (1 day)
Live Virtual
Language Time English 9:00AM – 5:00PM PDT Select delivery method/location (1 options) Live Virtual | 9:00AM – 5:00PM PDT- Enroll
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You will 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 case includes customer retention analysis to inform customer loyalty programs.
Skills Gained
- 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
Who Can Benefit
- Developers
- Data Scientists
Prerequisites
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Course Details
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
- Demo: SageMaker console
- Demo: 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
- Demo: Loading and Visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demo: Cleaning the data
Module 5: Training and Evaluating a Model
- Types of Algorithms
- 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
Module 6: Automatically Tune a Model
- Automatic hyperparameter 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 Scaling
- Demo: Configure and Test Autoscaling
- Demo: Check Hyperparameter tuning job
- Demo: AWS Autoscaling
- Exercise 10-11: Set up AWS Autoscaling
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
