Practical Data Science with Amazon SageMaker

In this course, you will:

• 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

This course is intended for:

• Developers
• Data Scientists

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

Please note: Effective 15th August 2022 the labs for all AWS courses will be delivered through AWS Builder labs. In order to access these labs you will need to have an Amazon account (used for Amazon.com/.co.uk retail). You can choose to use your existing Amazon account or you can elect to set up a new account utilising a new email address (such as Hotmail, Gmail, Yahoo etc etc). You can set up your new Amazon account here.

Please ensure that you have set up this Amazon account set up in advance of attending your class. Your Amazon account credentials will be used to access the AWS Builder lab environment that you will utilise during your course.

In order to access your digital course materials you are required to set up a Gilmore account in advance of attending your course. To do this please follow this link.