Example flows that demonstrate how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler.

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML. From a single interface in SageMaker Studio, you can import data from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker Feature Store, and in just a few clicks SageMaker Data Wrangler will automatically load, aggregate, and display the raw data. It will then make conversion recommendations based on the source data, transform the data into new features, validate the features, and provide visualizations with recommendations on how to remove common sources of error such as incorrect labels. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines or import that data into Amazon SageMaker Feature Store.

The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.

Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. Use this section to learn how to access and get started using Data Wrangler. Do the following:

• Complete each step in Prerequisites.

• Follow the procedure in Access Data Wrangler to start using Data Wrangler.

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Tabular dataset.

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Timeseries dataset.

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Joined dataset.

This example demonstrates how to select quantiles likely to maximize business profitability when using probabilistic time-series forecasting use cases.