Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities.
When you begin working on ML initiatives, you need to perform exploratory data analysis (EDA) or data preparation before proceeding with model building. Amazon SageMaker Data Wrangler is a capability of Amazon SageMaker that makes it faster for data scientists and engineers to prepare data for ML applications via a visual interface. Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes.
A key accelerator of feature preparation in Data Wrangler is the Data Quality and Insights Report. This report checks data quality and helps detect abnormalities in your data, so that you

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