After loading data into BigQuery and preprocessing features, what should you do next with BQML?

The most logical step after loading data into BigQuery and preprocessing features is to create an ML model inside BigQuery using BigQuery ML (BQML). This is because BQML is specifically designed to allow users to build and train machine learning models directly within the BigQuery environment without needing to export data. It allows for seamless integration of data analysis and machine learning, enabling users to leverage their existing SQL skills to define and iterate on models.

Creating an ML model involves specifying the structure and parameters of the model, selecting the right algorithm for the task, and training the model on the preprocessed data. Once the model is trained, you can evaluate its performance, which is crucial to ensure its effectiveness before deploying it for predictions or further analysis.

Other options like creating a visualization, analyzing the data further, or exporting the data might be relevant at different stages of the data analysis or machine learning process, but they are not the immediate next step after preprocessing. Creating a visualization or exporting data could come after model training to understand or utilize the model's predictions, while further analysis often precedes model creation to ensure that the right features and data insights are being used. Thus, building the ML model is the most direct and essential next step within the BQML