hands-on lab

Forecast Flight Delays with Amazon SageMaker

Up to 1h 30m
Get guided in a real environmentPractice with a step-by-step scenario in a real, provisioned environment.
Learn and validateUse validations to check your solutions every step of the way.
See resultsTrack your knowledge and monitor your progress.


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


February 13th, 2024 - Addressed data parsing issue and updated lab instructions

September 1st, 2023 - AWS resolved an issue causing training job validation to fail

July 5th, 2023 - Resolved training job creation issue

July 3rd, 2023 - Updated the instructions and screenshots to reflect the latest UI

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

Environment before

Environment after

About the author

Logan Rakai, opens in a new tab
Lead Content Developer - Labs
Learning paths

Logan has been involved in software development and research since 2007 and has been in the cloud since 2012. He is an AWS Certified DevOps Engineer - Professional, AWS Certified Solutions Architect - Professional, Microsoft Certified Azure Solutions Architect Expert, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Security Specialist (CKS), Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.

LinkedIn, Twitter, GitHub

Covered topics

Lab steps

Logging In to the Amazon Web Services Console
Understanding the Flight Delay Data
Creating a SageMaker Training Job
Creating a SageMaker Model
Creating a SageMaker Endpoint
Using the SageMaker Model for Inferences