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Introducing TabFM in BigQuery: Predictive analytics reimagined Vaibhav Sethi Group Product Manager Xi Cheng Engineering Manager 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. Here is what TabFM brings to your BigQuery analytics: - Zero-shot predictions: Skip model training, tuning, and artifact deployment. Simply pass your labeled historical data and new prediction tables into a single SQL function to get instant, high-quality predictions. - Predictive ML for your agentic applications: Building an agent for your business use? Add predictive powers to it with TabFM plus BigQuery MCP server. No runtimes or infrastructure to manage, just data in and predictions out. - State-of-the-art accuracy: Outperforms custom-trained, out-of-the-box traditional models on complex datasets, achieving superior accuracy scores on industry benchmarks. - Simple developer experience: Runs natively in BigQuery and is accessible via simple SQL syntax. Automatically handles featurization tasks such as missing values, categorical encoding, etc., with no complex feature engineering pipelines to manage. - Scalability: Processes massive inference tables (up to millions of rows) in minutes using BigQuery’s distributed inference architecture. The leading model for tabular predictions 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 models. 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. Learn more about the TabFM model here. Getting started with TabFM in BigQuery Using TabFM is straightforward. It is exposed directly through new, built-in SQL functions: AI.PREDICT and AI.EVALUATE. 1. 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.g. predicted_is_fraud). No manual feature engineering or model creation was required. 2. Evaluate models with AI.EVALUATE You can quickly check prediction performance against a test set using the AI.EVALUATE function. This allows you to generate standard evaluation metrics in a single step. 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!
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