BigQuery ML: CREATE MODEL Workflow Lifecycle

The CREATE MODEL workflow is the ordered lifecycle that moves data from a training query to a stored model and then to predictions. The workflow starts when CREATE MODEL reads training rows and ends when you inspect and explain the trained model. Default settings handle much of the work, while options let you tune the result.

1. Create the model: Run a CREATE MODEL statement that names the model, sets OPTIONS such as MODEL_TYPE, and supplies training data with a query.
2. Preprocess features: BigQuery ML applies automatic preprocessing, and you can add manual preprocessing inside the TRANSFORM clause.
3. Refine the model: Use hyperparameter options such as NUM_TRIALS to better fit the model to the training data.
4. Evaluate the model: Check performance on data outside the training set and compare it with other models.
5. Perform inference: Use prediction functions such as ML.PREDICT to score new data with the trained model.
6. Explain the model: Review explainability output to see how features influenced a prediction and the model overall.
7. Inspect model weights: Learn about model components such as learned coefficients or cluster centers.