The hands-on lab is part of these learning paths
Ready for the real environment experience?
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.
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
You should be familiar with:
- Basic S3 concepts
- Some knowledge of machine learning concepts is beneficial, but not required
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.