Historically, enterprise predictive analytics tasks such as predicting churn, purchase intent, or fraud scoring have meant building custom models using libraries like XGBoost, Random Forest, or Deep Neural Networks (DNNs). While effective, the traditional train-tune-deploy-retrain cycle can be complex and time-consuming. Additionally, the overhead of manual feature engineering, hyperparameter tuning, lengthy and expensive training, and the need for specialized data science skills can lead businesses to underutilize predictive models in their decision-making. Today, we are announcing the TabFM model in BigQuery.
Developed by Google Research, TabFM is a state-of-the-art, pre-trained foundation model for regression and classification on tabular data. It leverages in-context learning (ICL) to deliver highly accurate predictions on your tabular datasets instantly via a single SQL statement, removing the separate training and deployment steps. TabFM on BigQuery is currently in preview. Google’s TabFM delivers industry-leading accuracy across a wide range of tabular data. In evaluations on the TabArena benchmark, TabFM consistently outperforms both classic machine learning models and other tabular foundation mod els.
ELO ratings (↑) for the top 10 models across TabArena classification (upper) and regression (lower). (D) = default; (T+E) = tuned + ensemble. Higher scores denote superior performance. Using TabFM is straightforward. It is exposed directly through new, built-in SQL functions: AI. Get instant predictions with AI. PREDICT To make predictions, you write a single query that passes your training data and prediction data. The model automatically infers whether the task is a classification or regression problem based on the data type of your target label.
In this example, the output contains all original columns from your prediction table plus predicted label and probability columns (e. No manual feature engineering or model creation was required. EVALUATE You can quickly check prediction performance against a test set using the AI. This allows you to generate standard evaluation metrics in a single step. 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 cla