/bigquery-ai-ml

name: bigquery-ai-ml
metadata:
  version: "1.1.2"
  category: AiAndMachineLearning
  publisher: google
description: >-
  Leverages BigQuery's built-in machine learning and GenAI capabilities
  for advanced data analytics. Use when you need to write SQL queries
  that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers,
  perform semantic search or vector search, classify text, calculate similarity,
  summarize content, translate language, evaluate models, filter by semantic conditions,
  measure the causal effect of an intervention, compute correlations between columns,
  generate descriptive statistics and table distribution profiles,
  detect change points or structural breaks, extract trend or seasonality components,
  or leverage generative AI capabilities in BigQuery. Do not use for general
  BigQuery dataset, table, or job management requests.

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`.

Attribution

**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you MUST read [Usage Attribution](references/attribution.md). For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix `gcloud` with `CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:bigquery-ai-ml)"` and append `agent-skills/1.0 (skill:bigquery-ai-ml)` to `User-Agent` headers.

Function Selection & Reference Directory

Function | Reference | SQL Shape | Requires `CREATE MODEL` or `CREATE VECTOR INDEX`? | When to Use & Disambiguation :---------------------------- | :-------------------------------------------------------------------- | :------------- | :------------------------------------------------ | :--------------------------- **`AI.AGG`** | [ai_agg.md](references/ai_agg.md) | `Aggregate` | No | Multi-row semantic aggregation with `GROUP BY`; synthesizes one summary string across many rows (unlike per-row `AI.GENERATE`). **`AI.CAUSAL_EFFECT`** | [ai_causal_effect.md](references/ai_causal_effect.md) | `TVF` | No | Quantifies counterfactual post-intervention impact on a time series after a specific treatment timestamp. **`AI.CLASSIFY`** | [ai_classify.md](references/ai_classify.md) | `Scalar` | No | Semantic classification of text or multimodal unstructured data into predefined category labels. **`AI.DETECT_ANOMALIES`** | [ai_detect_anomalies.md](references/ai_detect_anomalies.md) | `TVF` | No | Zero-shot **TimesFM** detector for isolated time-series point anomalies/outliers (use `ML.DETECT_CHANGE_POINTS` for sustained shifts). **`AI.EVALUATE`** | [ai_evaluate.md](references/ai_evaluate.md) | `TVF` | No | Zero-shot **TimesFM** time-series forecast evaluation/backtesting against a historical holdout split. **`AI.FORECAST`** | [ai_forecast.md](references/ai_forecast.md) | `TVF` | No | Zero-shot **TimesFM** time-series forecasting without training a custom model. **`AI.GENERATE`** | [ai_generate.md](references/ai_generate.md) | `Scalar` | No | Default for per-row LLM text or multimodal structured extraction (`output_schema`); returns `STRUCT`. Prefer over `AI.GENERATE_TABLE`. **`AI.GENERATE_EMBEDDING`** | [ai_generate_embedding.md](references/ai_generate_embedding.md) | `TVF` | Yes | Batch generates `ARRAY<FLOAT64>` vector embeddings for text or visual content using a remote embedding model. **`AI.GENERATE_TABLE`** | [ai_generate_table.md](references/ai_generate_table.md) | `TVF` | Yes | Table-valued structured extraction (`FROM` clause); only use instead of `AI.GENERATE` when a remote model + TVF is explicitly needed. **`AI.IF`** | [ai_if.md](references/ai_if.md) | `Scalar` | No | Evaluates boolean semantic conditions (`BOOL`) on text or multimodal data for `WHERE`/`CASE`/`JOIN` filtering (do not use `AI.GENERATE_TABLE`). **`AI.KEY_DRIVERS`** | [ai_key_drivers.md](references/ai_key_drivers.md) | `TVF` | No | Default one-step attribution of dimensional segments driving metric changes (`interest_label_col` `TRUE` vs. `FALSE`). Prefer over `CONTRIBUTION_ANALYSIS`. **`AI.PREDICT`** | [ai_predict.md](references/ai_predict.md) | `TVF` | No | Zero-shot **TabFM** foundation model for tabular classification or regression without training and managing custom models. **`AI.SCORE`** | [ai_score.md](references/ai_score.md) | `Scalar` | No | Assigns a continuous `FLOAT64` semantic score to text or multimodal data for ranking or sorting (`ORDER BY`). **`AI.SEARCH`** | [ai_search.md](references/ai_search.md) | `TVF` | No