What is BigQuery ML?
BigQuery ML enables training and running machine learning models directly in BigQuery using SQL syntax. Data analysts can build models without exporting data or learning Python. Models train on BigQuery data and deliver predictions as SQL queries.
Core Features
- SQL-based Training: CREATE MODEL with SQL syntax for model training
- Integrated Predictions: ML.PREDICT for batch and real-time predictions
- Automatic Feature Engineering: Automatic transformation of input data
- Model Registry: Versioning and management of trained models
- Vertex AI Integration: Export models to Vertex AI for extended deployment options
Typical Use Cases
Churn Prediction
SQL analysts create churn models on customer data. Training happens with CREATE MODEL, predictions with ML.PREDICT. No data science expertise required.
Demand Forecasting
ARIMA_PLUS models forecast time series like revenue or demand. Models automatically detect seasonality and trends in historical data.
Recommendation Systems
Matrix Factorization creates recommendations from user-item interactions. Product recommendations and content personalization directly on BigQuery data.
Benefits
- No data export or ETL effort
- SQL knowledge sufficient for simple models
- Scales automatically with BigQuery infrastructure
- Seamless integration with BI tools
Integration with innFactory
As a Google Cloud Partner, innFactory supports you with BigQuery ML: use case identification, model design, feature engineering and integration of ML predictions into business processes. We help with the decision between BigQuery ML and Vertex AI.
Available Tiers & Options
BigQuery ML
- No data export needed
- SQL-based
- Automatic feature engineering
- Limited model types
- Less control than Vertex AI
Typical Use Cases
Technical Specifications
Frequently Asked Questions
What is BigQuery ML?
BigQuery ML enables training machine learning models directly in BigQuery using SQL. Data does not need to be exported and SQL knowledge is sufficient for simple ML models.
Which model types are supported?
BigQuery ML supports linear and logistic regression, Boosted Trees (XGBoost), Deep Neural Networks, K-Means Clustering, Matrix Factorization and ARIMA for time series.
How does BigQuery ML differ from Vertex AI?
BigQuery ML is optimized for SQL-based ML directly on BigQuery data. Vertex AI offers more control, custom training and MLOps features for more complex requirements.
Can I use TensorFlow models in BigQuery ML?
Yes, BigQuery ML can import TensorFlow SavedModels and use them for predictions. Complex models are trained in Vertex AI and deployed to BigQuery ML.
