Generate Machine Learning Predictions Without Writing Code
What you will accomplish
In this tutorial, you will:
- Import datasets
- Select the target variable for classification
- Inspect datasets visually
- Build an ML model with the SageMaker Canvas Quick Build feature
- Understand model features and metrics
- Generate and understand bulk and single predictions
Before starting this tutorial, you will need:
- An AWS account: If you don't already have an account, follow the Setting Up Your AWS Environment getting started guide for a quick overview.
In this tutorial, you will build an ML model that can predict the estimated time of arrival (ETA) of shipments (measured in days). You will use a dataset that contains complete shipping data for delivered products, including estimated time, shipping, priority, carrier, and origin.
You have successfully used Amazon SageMaker Canvas to import and prepare a dataset for ML from Amazon S3, select the target variable, build an ML model using the quick build mode, and use the visual interface.