Forecast Flight Delays with Amazon SageMaker

The hands-on lab is part of this learning path

Lab Steps

keyboard_tab
lock
Logging in to the Amazon Web Services Console
lock
Understanding the Flight Delay Data
lock
Creating a SageMaker Training Job
lock
Creating a SageMaker Model
lock
Creating a SageMaker Endpoint
lock
Using the SageMaker Model for Inferences
lock
Validate Forecast Flight Delays with Amazon SageMaker Lab

Ready for the real environment experience?

DifficultyBeginner
Duration1h 30m
Students45

Description

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 
Environment before
PREVIEW
arrow_forward
Environment after
PREVIEW

About the Author

Students27850
Labs83
Courses10
Learning paths6

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 Administrator (CKA), Certified Kubernetes Application Developer (CKAD), Linux Foundation Certified System Administrator (LFCS), and Certified OpenStack Administrator (COA). He earned his Ph.D. studying design automation and enjoys all things tech.