# Bigquery AI Ml

> 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, detect outliers, find key drivers, or leverage generative AI capabilities in BigQuery.

- Skill: `gemini-cli-extensions-data-agent-kit-starter-pack/bigquery-ai-ml` (Agent Skill, multi-file: 18 files)
- Install (CLI): `npx skillmds@latest add gemini-cli-extensions-data-agent-kit-starter-pack/bigquery-ai-ml`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gemini-cli-extensions-data-agent-kit-starter-pack/bigquery-ai-ml/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: gemini-cli-extensions (https://skillmd.com/u/gemini-cli-extensions-data-agent-kit-starter-pack)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/gemini-cli-extensions-data-agent-kit-starter-pack/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`.

> [!IMPORTANT]
> You MUST read and follow the global constraints and mandatory function routing rules in
> [ai_function_best_practices.md](references/ai_function_best_practices.md) before writing any BQML AI/ML SQL query.

## Reference Directory

-   **Best Practices**:
    [ai_function_best_practices.md](references/ai_function_best_practices.md)

-   **Functions Reference**:

    -   **AI.AGG**: [ai_agg.md](references/ai_agg.md) - Multi-row semantic
        aggregation and summarization.
    -   **AI.CLASSIFY**: [ai_classify.md](references/ai_classify.md) -
        Classify text.
    -   **AI.DETECT_ANOMALIES**:
        [ai_detect_anomalies.md](references/ai_detect_anomalies.md) -
        Detect anomalies.
    -   **AI.EVALUATE**: [ai_evaluate.md](references/ai_evaluate.md) -
        Evaluate models.
    -   **AI.FORECAST**: [ai_forecast.md](references/ai_forecast.md) -
        Time-series forecasting.
    -   **AI.GENERATE**: [ai_generate.md](references/ai_generate.md) -
        Generate text using LLMs.
    -   **AI.GENERATE_EMBEDDING**:
        [ai_generate_embedding.md](references/ai_generate_embedding.md) -
        Generate embeddings.
    -   **AI.GENERATE_TABLE**:
        [ai_generate_table.md](references/ai_generate_table.md) -
        Table-valued AI generation.
    -   **AI.IF**: [ai_if.md](references/ai_if.md) - Evaluate semantic
        conditions.
    -   **AI.KEY_DRIVERS**:
        [ai_key_drivers.md](references/ai_key_drivers.md) - Identifies key
        drivers, this is a TVF.
    -   **AI.SCORE**: [ai_score.md](references/ai_score.md) - Score data.
    -   **AI.SEARCH**: [ai_search.md](references/ai_search.md) - Semantic
        search.
    -   **AI.SIMILARITY**:
        [ai_similarity.md](references/ai_similarity.md) - Semantic
        similarity.
    -   **Remote Models**:
        [remote_models.md](references/remote_models.md) - Working with
        remote models (Vertex AI).
    -   **CONTRIBUTION_ANALYSIS**:
        [ml_contribution_analysis.md](references/ml_contribution_analysis.md)
        -   Finds contributing factors, key drivers of change. Requires creating
            a MODEL entity.
    -   **VECTOR_SEARCH**:
        [vector_search.md](references/vector_search.md) - Vector search
        best practices.