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, 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, 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.
摘要
- 用途
- 本技能說明如何在 BigQuery SQL 中運用機器學習與生成式 AI 功能（包括 Vertex AI 整合）進行資料分析。
- 使用時機
- 當您需要在 BigQuery SQL 中進行 AI／機器學習分析時使用；若要管理資料集、資料表或工作，則不適用。
- 輸出結果
- 內容提供相關函式與模型的參考，協助產生預測、異常偵測、分類、摘要、嵌入向量及語意或向量搜尋結果。
上游變更（6 筆，最新收錄 2026-09-21）
d6b9f75 Add correlation, change point, causal effect, trend, and seasonality references to bigquery-ai-ml. 技能描述與參考目錄現涵蓋因果效應、相關性、變化點偵測，以及趨勢與季節性擷取，且中繼資料版本更新為 1.1.0。 7322b87 Add new BQ AI/ML references. 技能描述擴大了適用情境，參考目錄則改為函式清單，並加入分類、評估、嵌入、搜尋、相似度、關鍵驅動因素與向量搜尋等 AI/ML 功能指南。 1c3a246 Add AI.KEY_DRIVERS to bigquery-ai-ml 技能描述與概述將 AI.KEY_DRIVERS 納入所述功能，參考目錄也新增 AI Key Drivers 項目。 c75b500 Restructure the YAML frontmatter in all SKILL.md files to nest the 'category' field under the 'metadata:' block. The 'category' field is used to organize the skills in the README.md 技能的前置中繼資料新增一個區塊，其中包含 AiAndMachineLearning 類別。 872e31b Refactor BQ skills 此變更建立 BigQuery AI & ML 技能檔案，加入描述、預測、異常偵測與文字生成的參考資料，以及 BigQuery Basics 技能的連結。 npx skills add "lettucebo/Skills#v2.2.0@bigquery-ai-ml" --full-depth ⚠ v2.2.0 發布後即可使用
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.
Related Skills
- BigQuery Basics Skill: SKILL.md file for core BigQuery concepts, resource management, CLI, and client libraries.