SQL for Machine Learning

Patterns for extracting ML-ready features from SQL Server — turning normalized relational data into the wide, denormalized, NULL-free datasets that models consume.

When to Use

• Building a feature table or training dataset from application data
• Feature engineering in T-SQL (rolling aggregations, lag features, RFM scoring)
• Encoding categorical variables (one-hot, ordinal, frequency encoding)
• Handling NULLs for ML (imputation strategies in SQL)
• Sampling and train/test splitting from SQL Server
• Exporting large datasets to CSV, Parquet, or pandas
• Preventing data leakage in temporal feature queries
• Writing queries that feed scikit-learn, XGBoost, or PyTorch pipelines

When NOT to use: application schema design (table design, naming conventions, access control), query performance tuning (execution plans, index tuning, wait stats), BI dashboards and summary reports (GROUPING SETS, pivot tables, dashboard queries), or running R/Python inside SQL Server (SQL Server ML Services).

Feature Table Build Workflow