Course 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.
Course 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
Who Should Attend?
Developers and Data Scientists.
- Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
- Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
- Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
- Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
- Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
- Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.
Agenda
1 - Introduction to Machine Learning
- Types of ML
- Job Roles in ML
- Steps in the ML pipeline
2 - Introduction to Data Prep and SageMaker
- Training and Test dataset defined
- Introduction to SageMaker
- Demonstration: SageMaker console
- Demonstration: Launching a Jupyter notebook
3 - Problem formulation and Dataset Preparation
- Business Challenge: Customer churn
- Review Customer churn dataset
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
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
6 - Automatically Tune a Model
- Automatic hyperparameter tuning with SageMaker
- Exercises 6-9: Tuning Jobs
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
8 - Relative Cost of Errors
- Cost of various error types
- Demo: Binary classification cutoff
9 - Amazon SageMaker architecture and features
- Accessing Amazon SageMaker notebooks in a VPC
- Amazon SageMaker batch transforms
- Amazon SageMaker Ground Truth
- Amazon SageMaker Neo