Prepare data from Snowflake for machine learning with Amazon SageMaker Data Wrangler

Data preparation remains a major challenge in the machine learning (ML) space. Data scientists and engineers need to write queries and code to get data from source data stores, and then write the queries to transform this data, to create features to be used in model development and training. All of this data pipeline development work doesn't really focus on the building of ML models, but focuses on the building of data pipelines necessary to make the data available to the models. Amazon SageMaker Data Wrangler makes it easier for data scientists and engineers to prepare data in the early phase of developing ML applications by using a visual interface. Data Wrangler comes with over 300 built-in data transformations to help normalize, transform, and combine features without writing any code. You can now use Snowflake as a data source in Data Wrangler to easily prepare data in Snowflake for ML.