BigQuery ML
Build, evaluate, and deploy machine learning models directly in Google BigQuery using SQL, from classification and regression to time-series forecasting and recommendations.
SupaScore
83.95Best for
- ▸Build customer churn prediction models using SQL directly in BigQuery
- ▸Create demand forecasting models with ARIMA_PLUS for time series data
- ▸Deploy XGBoost-based recommendation systems without leaving the data warehouse
- ▸Implement real-time fraud detection using classification models on streaming data
- ▸Develop customer lifetime value prediction models with automated feature engineering
What you'll get
- ●Complete CREATE MODEL statements with TRANSFORM clauses for feature engineering and optimized hyperparameters
- ●Model evaluation queries showing precision, recall, and ROC-AUC with business interpretation
- ●Production-ready ML.PREDICT queries with batch scoring pipelines and monitoring recommendations
Not designed for ↓
- ×Complex deep learning models requiring custom architectures beyond DNN
- ×Real-time model serving outside of BigQuery's batch prediction capabilities
- ×Computer vision or natural language processing tasks requiring specialized preprocessing
- ×Models requiring extensive hyperparameter tuning beyond BQML's built-in options
Clean structured data in BigQuery tables with clear business objectives, defined target variables, and sufficient historical data for training.
Complete SQL-based ML pipelines with model creation, evaluation metrics, prediction queries, and deployment recommendations for production use.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (5 official docs, 3 books)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial release
Prerequisites
Use these skills first for best results.
Works well with
Need more depth?
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Common Workflows
End-to-End BQML Pipeline
Data exploration and preparation, model building in BQML, then production deployment with monitoring
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