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Practical Data Science with Amazon SageMaker
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Course Description
Overview
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.- Duration: 1 day
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
Audience
- Developers
- Data Scientists
Prerequisites
-
We recommend that attendees of this course have:
- Familiarity with Python programming language
- Basic understanding of Machine Learning
Topics
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
- Training and test dataset defined
- Introduction to SageMaker
- Demonstration: SageMaker console
- Demonstration: Launching a Jupyter notebook
- Business challenge: Customer churn
- Review customer churn dataset
- Demonstration: Loading and visualizing your dataset
- Exercise 1: Relating features to target variables
- Exercise 2: Relationships between attributes
- Demonstration: Cleaning the data
- 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
- Automatic hyper parameter tuning with SageMaker
- Exercises 6-9: Tuning jobs
- 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
- Cost of various error types
- Demo: Binary classification cutoff
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo
Related Courses
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The Machine Learning Pipeline on AWS
AWS-185- Duration: 4 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,700.00 USD
-
MLOps Engineering on AWS
AWS-245- Duration: 3 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,025.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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