BigQuery ML

Train and run machine learning models inside BigQuery using SQL

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What is BigQuery ML?

BigQuery ML lets you create, evaluate and predict with machine learning models using SQL statements inside Google BigQuery, so no data has to leave the warehouse and no separate training environment has to be maintained. Supported model types cover linear and logistic regression, k-means clustering, matrix factorisation, time series forecasting, boosted trees and imported TensorFlow or ONNX models. It integrates with Vertex AI for deeper experimentation and exposes Gemini-backed functions for text generation and embeddings. The appeal is unglamorous but real: analysts who already know SQL can ship a working forecasting or churn model without learning a new framework.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

CREATE MODEL statements for regression, classification and forecasting
K-means clustering, matrix factorisation and PCA built in
Import TensorFlow and ONNX models and run predictions in SQL
Forecasting with ARIMA_PLUS and automatic seasonality detection
Gemini-backed text generation, translation and embedding functions
Integration with Vertex AI for AutoML and deeper model tuning

Pros & cons

Strengths

No data movement - training and scoring happen where the data lives
Analysts who know SQL can build models without a Python stack
Model evaluation and explainability functions are built in

Watch out for

Limited to the model families Google supports natively
Costs are query-based and training runs consume real budget
Deep custom architectures still belong in a full ML framework

Best for & use cases

sql-based machine learning, forecasting, churn prediction and warehouse-native modelling

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FAQ

Do I need to know machine learning to use it?

You need to understand what you are predicting and how to judge a model, which matters more than framework knowledge. The syntax is SQL, and automatic options handle preprocessing, but picking sensible features and reading evaluation metrics are still necessary skills.

What is the honest limitation compared with Vertex AI?

BigQuery ML covers common tabular problems well and keeps everything in the warehouse. Once you need custom architectures, distributed training, or fine-grained hyperparameter search, Vertex AI or an external framework is the right place to work.

How do costs behave in practice?

Training and prediction are billed by the data processed, so iterating on a large table gets expensive quickly. Sampling during development and materialising intermediate tables usually cuts the bill substantially.