AI.EVALUATE returns a robust set of metrics such as r2_score, mean_absolute_error etc. for regression problems and metrics such as precision, recall, and f1 for classification problems.
TabFM in BigQuery under the hood
Traditional machine learning requires fitting model parameters to a training dataset. TabFM, in contrast, uses in-context learning. Similar to how large language models (LLMs) learn a task from few-shot examples in a prompt, TabFM reads your training table as in-context examples and generates predictions for your target table in a single forward pass.
To handle the computational complexity and memory footprint of tabular foundation models, BigQuery performs distributed, parallelized inference on your data. Further, to optimize performance and resource utilization, it uses intelligent training-data sampling as well as distributed execution. This allows BigQuery to handle large input rows for training data while executing predictions quickly and efficiently across millions of rows of inference data.
Choosing the right tool for the job
TabFM introduces groundbreaking zero-shot capabilities to BigQuery, and complements existing offerings such as XGBoost models. Here’s how to choose between TabFM and other models:
-
Use TabFM when you need rapid, high-quality predictive insights without machine learning expertise, when historical datasets are small-to-medium sized, when data changes frequently, and when you need to retrain your models frequently to maintain accuracy. It is also a great fit for conversational or agentic workflows where you need predictive analysis on demand.
-
Use traditional models like XGBoost when you have very large historical datasets, require complete control over custom hyperparameter tuning, have a high number of features that exceed current limits of TabFM, or need feature-importance explainability, i.e., which of the input features contributed most to the prediction.
Predictive machine learning made easy
With TabFM natively integrated into BigQuery, predictive ML is now as easy as running a standard SELECT query. By eliminating the manual overhead of model training, tuning, and management, TabFM lets developers, data scientists and analysts go from raw data to rich predictive insights in seconds.
To get started today, check out the public documentation. For questions or feedback reach out to our team at bqml_feedback@google.com. We look forward to seeing what you build!
