The Scenario
A model governance review is coming up Thursday. Your ML platform lead handed off a dataset — my-project.ml_models — and asked you to build a model inventory before the meeting: model names, types, training dates, evaluation metrics, hyperparameters. All twelve models. In a shared Excel workbook.
She left for a conference Wednesday morning. The handoff document says "you can pull this from BigQuery." It does not say how.
The bad version:
- Search BigQuery documentation for the INFORMATION_SCHEMA views that expose ML model metadata.
- Write a query that joins BQML_MODELS and BQML_TRAINING_INFO for the right project and dataset, run it in the BigQuery console, notice that hyperparameter columns come back as JSON structs that paste as raw JSON into Excel.
- Manually parse each struct into columns. Do this twelve times.
This is not analysis work. It is data extraction and reformatting — and Thursday is not very far away.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook. It reads the workbook and talks to Google BigQuery directly — including querying BigQuery ML model metadata and writing it into structured columns.
Fetch details for all BigQuery ML models in my project dataset and export their names, training loss, evaluation loss, and hyperparameters into my Excel workbook
What You Get
- One row per model, with model name, model type, training completion date, and the evaluation metrics BigQuery exposes for that model type.
- Metrics land as separate columns — not as a JSON blob — so you can sort, filter, or format them without further processing.
- If a model has no evaluation data yet, that's noted in the relevant cell rather than crashing the output.
What If the Data Is Not Quite Ready
You also need hyperparameter values per model as separate columns
Fetch all BigQuery ML models in my dataset my-project.ml_models and for each one write the model name, type, training date, training loss, evaluation loss, and each hyperparameter as its own column in my Excel workbook.
You want to filter to only regression models
List all BigQuery ML models in my-project.ml_models where the model type is a regression model (LINEAR_REG or BOOSTED_TREE_REGRESSOR), and write the model name, type, training date, and mean squared error metric into my Excel workbook.
Add a column flagging models that haven't been retrained in 90 days
Fetch all BigQuery ML models in my-project.ml_models, write name, type, training date, and evaluation metrics into my Excel workbook, and add a column called 'retrain_flag' — mark 'overdue' for any model whose training date is more than 90 days ago, otherwise leave it blank.
Pull the inventory, rank models by evaluation loss, and note the best performer
Fetch all BigQuery ML models in my-project.ml_models, write their names, types, training dates, and evaluation loss into my Excel workbook sorted by evaluation loss ascending, and write 'best model' into a column called 'notes' for the row with the lowest evaluation loss.
Inventory, ranking, and annotation — one prompt, one pass.
Try It
Get the 7-day free trial of SheetXAI and open an Excel workbook, then ask it to pull your BigQuery ML model inventory before your next governance review. You can also audit the full dataset schema or return to the Google BigQuery integration overview.