The article co-authored by Karen Yuan outlines how the Couchbase Data Platform can streamline the data science workflow, emphasizing the utility of its Query and Analytics services for exploratory data analysis (EDA) and efficient data handling. By using Couchbase, data scientists can perform EDA directly within the database, minimizing memory usage and network data transfer, which is particularly advantageous for large datasets. The piece uses a customer churn prediction model as a case study to demonstrate the process of problem definition, data collection, and preparation, followed by EDA and model training, highlighting the efficiency gained by using Couchbase's integrated features. The article further details storing machine learning models and metadata within Couchbase, reducing the complexity and number of tools typically required in the data science process. It concludes by suggesting resources for further exploration of machine learning in conjunction with Couchbase.