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Data preparation Using Amazon SageMaker and AWS Glue DataBrew

March 22, 2022

Machine learning helps us find patterns in data—we then use the patterns to make predictions about new data points. Machine learning models are only as good as the data that is used to train them. After the data is collected, the integration, annotation, preparation, and processing of that data is critical. An essential characteristic of suitable training data is that it is provided in a way that is optimized for learning and generalization. In this session, we go over Amazon SageMaker Data Wrangler and AWS Glue DataBrew offerings and learn how to prepare your data for ML. We explain the process of cleaning and transforming raw data prior to processing and analysis. Data preparation should start with a small, statistically valid sample, and iteratively be improved with different data preparation strategies, while continuously maintaining data integrity. We show how Amazon SageMaker Suite provides multiple features which helps us construct the dataset and transform the data.
Gaurav Sahi, Senior Manager, Solutions Architect, AISPL
Kamal Manchanda, Solutions Architect, AISPL

Download slidesdemo 1 and demo 2

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