BigQuery AI Functions in Pandas
Imagine running advanced machine learning workflows—from forecasting and classification to high-dimensional embedding generation—without ever leaving your familiar pandas DataFrame. With the new .bigquery accessor from BigQuery DataFrames (BigFrames), that vision is now a reality. You can seamlessly invoke BigQuery's enterprise-grade AI functions directly within pandas, scaling your data science and ML pipelines on cloud infrastructure .
Take linear regression as an example. Traditionally, a pandas user who needs to run regression would have to extract data locally, manually select features, and train a custom scikit-learn model like this:
Now, with the .bigquery accessor, you can bypass the local training process entirely and leverage powerful ai.predict() directly on your data in BigQuery:
As you can see, the ai.predict() is able to perform regression tasks directly over two pandas dataframes with a single function call, which greatly simplifies the entire ML process.
How the BigQuery accessor works
Under the hood, the...
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