This document discusses implementing practical data science on AWS. It covers analyzing datasets and training ML models with AutoML, registering data with AWS Glue and querying with Athena, and performing data visualization. It also discusses measuring statistical bias with SageMaker Clarify and using SHAP to determine feature importance. The document recommends exploring datasets with AWS Data Wrangler and Glue and building, training, and deploying BERT pipelines using feature engineering, preprocessing, and the feature store. It concludes by discussing optimizing models with techniques like Hyperparameter tuning and deploying human-in-the-loop pipelines using SageMaker Ground Truth and Augmented AI.