SQL in AI and Machine Learning

SQL is the bridge between raw data and AI/ML models.

How SQL powers ML:

• Data preparation - clean and transform training data
• Feature engineering - create input variables for models
• Training data extraction - select and label datasets
• Model evaluation - query predictions vs actuals

Tools combining SQL and ML:

• BigQuery ML - train models with SQL syntax
• Snowpark - run ML in Snowflake
• Amazon Redshift ML - SQL-based predictions

Most ML engineers spend 80% of their time on data preparation. SQL is essential for that work.

You don't need to choose between SQL and Python - the best data scientists use both.