bigquery-ai-ml

BigQuery AI & ML

BigQuery integrates with Vertex AI to provide powerful machine learning and generative AI capabilities directly within SQL queries using built-in functions like AI.FORECAST, AI.KEY_DRIVERS, AI.DETECT_ANOMALIES, and AI.GENERATE.

Reference Directory

• Functions Reference:

  • AI.AGG: ai_agg.md - Multi-row semantic aggregation and summarization.
  • AI.CAUSAL_EFFECT: ai_causal_effect.md - Quantifies the impact of an intervention on a time series.
  • AI.CLASSIFY: ai_classify.md - Classify text.
  • AI.DETECT_ANOMALIES: ai_detect_anomalies.md - Detect anomalies.
  • AI.EVALUATE: ai_evaluate.md - Evaluate models.
  • AI.FORECAST: ai_forecast.md - Time-series forecasting.
  • AI.GENERATE: ai_generate.md - Generate text using LLMs.
  • AI.GENERATE_EMBEDDING: ai_generate_embedding.md - Generate embeddings.
  • AI.GENERATE_TABLE: ai_generate_table.md - Table-valued AI generation.
  • AI.IF: ai_if.md - Evaluate semantic conditions.
  • AI.KEY_DRIVERS: ai_key_drivers.md - Identifies key drivers, this is a TVF.
  • AI.SCORE: ai_score.md - Score data.
  • AI.SEARCH: ai_search.md - Semantic search.
  • AI.SIMILARITY: ai_similarity.md - Semantic similarity.
  • Remote Models: remote_models.md - Working with remote models (Vertex AI).
  • CONTRIBUTION_ANALYSIS: ml_contribution_analysis.md
    • Finds contributing factors, key drivers of change. Requires creating a MODEL entity.
  • ML.CORRELATION: ml_correlation.md - Calculates correlation between columns, optionally sliced by dimensions.
  • ML.DETECT_CHANGE_POINTS: ml_detect_change_points.md - Detects structural breaks or sustained shifts in a time series.
  • ML.SEASONALITY: ml_seasonality.md - Extracts seasonal components from a time series.
  • ML.TREND: ml_trend.md - Extracts the long-term trend component from a time series.
  • VECTOR_SEARCH: vector_search.md - Vector search best practices.