This lab uses Amazon SageMaker to create a machine learning model that forecasts flight delays for US domestic flights. Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow. The lab does not require any data science or developer experience to complete. You will focus on the easy-to-use SageMaker interface for creating machine learning models using built-in algorithms with relevant concepts explained along the way. You will use US Department of Transportation flight data to train the model that forecasts flight delays. This lab uses SageMaker to create a regression model. Regression models predict a continuous variable, as opposed to a set of classes that are predicted by classification models. You will finish the lab by creating a script that retrieves real-time inference from SageMaker.

Lab Objectives

Upon completion of this lab you will be able to:

• Train models using built-in SageMaker algorithms
• Create SageMaker models
• Deploy SageMaker endpoints to get real-time inferences from your models
• Explain different machine learning concepts such as model types, data encoding, and training and test sets

Lab Prerequisites

You should be familiar with:

• Basic S3 concepts
• Some knowledge of machine learning concepts is beneficial, but not required

Updates

December 19th, 2022 - Updated instructions to include new links

November 4th, 2022 - Updated screenshots and instructions to match UI changes

April 16th, 2021 - Moved validation checks to the most relevant lab step for more immediate validation feedback