Diagnose Cancer with an Amazon Machine Learning Classifier

Lab Steps

Logging in to the Amazon Web Services Console
Understanding the Diagnostic Cancer Data
Creating an Amazon Machine Learning Datasource
Creating an Amazon Machine Learning Model
Inspecting the Amazon Machine Learning Datasource
Evaluating the Amazon Machine Learning Model
Creating Diagnoses with Amazon Machine Learning Model Predictions

Ready for the real environment experience?



Lab Overview

Binary classification is a kind of machine learning that predicts whether an item belongs to one of two classes. Example applications of binary classification are predicting if an email is spam or not, if a user will buy a new product, or determining if a tissue sample is benign or malignant. In this Lab, you will learn about binary classification and model evaluation in AWS as you diagnose cancer with an Amazon Machine Learning binary classifier. You will train a model with medical data, evaluate the model's performance, and use the model to make diagnoses.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Create Amazon Machine Learning datasources, and binary classification models
  • Understand model how recipes can be used to transform data
  • Interpret model evaluation results and how model parameters impact them
  • Perform real-time predictions with the model

Lab Prerequisites

You should be familiar with:

  • Basic Amazon S3 concepts

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:

About the Author

Learning paths4

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, MCSE: Cloud Platform and Infrastructure, Google Cloud Certified Associate Cloud Engineer, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), and Linux Foundation Certified System Administrator (LFCS). He earned his Ph.D. studying design automation and enjoys all things tech.