Data transformation
Data processing for ML primarily includes data transformation. At its core, SageMaker Data Wrangler includes over 300 built-in transformations that are commonly used for cleaning, transforming, and featurizing your data specifically for data science and ML. Using these built-in transformations, you can transform columns within your dataset without having to write any code. In addition to these built-in transformations, you can add custom transformations using PySpark, Python, pandas, and PySpark SQL. Some of these transformations operate in place, while others create a new output column in your dataset. Whenever you incorporate a transform into your data flow, it introduces a new step in the process. Each added transform modifies your dataset and generates a fresh data frame as a result. Subsequently, any subsequent transforms you apply will be performed on this updated data frame. In the real world, datasets are often imbalanced. This imbalance can be in the form...
