Ready to reduce the time it takes to aggregate and prepare data for machine learning (ML)? You can with Amazon SageMaker Data Wrangler.

Many organizations have been using AWS Glue, AWS Data Pipeline, and other tools to prepare their ML data. However, with SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering and complete each step of the data preparation workflow, including data selection, cleaning, exploration, and visualization from a single visual interface. Using SageMaker Data Wrangler’s data selection tool, you can choose the data you want from various data sources and import it with a single click. AWS Advanced Partner, JBS can help you get started!

In this complimentary half-day learning session, JBS senior data architects will show you how to use SageMaker Data Wrangler by walking through a use case and demoing all of the essential areas of the tool for building a pipeline. This includes a high-level overview of the tool, its use, and its integration into the larger data structure. We will also provide you with content for deeper technical discussions and specific topics so you walk away with a comprehensive understanding of the tool. Finally, we will package up all of the code/data used during the demo in a Cloud Formation template, allowing anybody to build, run, and play with it.

Specifically, in this session, we will:

• Upload and ingest datasets from AWS S3 into SageMaker Data Wrangler

• Analyze this dataset using SageMaker Data Wrangler analyses and perform feature engineering activities

• Define a data flow using SageMaker Data Wrangler data transforms

• Export the flow to a Jupyter Notebook that you can use to create a SageMaker Data Wrangler job

• Process the data and kick off a SageMaker training job