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
- 本技能说明如何在 BigQuery SQL 中运用机器学习与生成式 AI 功能（包括 Vertex AI 集成）进行数据分析。
- 使用时机
- 当您需要在 BigQuery SQL 中进行 AI／机器学习分析时使用；若要管理数据集、数据表或工作，则不适用。
- 输出结果
- 内容提供相关函数与模型的参考，协助产生预测、异常侦测、分类、摘要、嵌入矢量及语意或矢量搜索结果。
上游变更（6 条，最新收录 2026-09-21）
d6b9f75 Add correlation, change point, causal effect, trend, and seasonality references to bigquery-ai-ml. 技能描述与参考目录现涵盖因果效应、相关性、变化点侦测，以及趋势与季节性截取，且中继数据版本更新为 1.1.0。 7322b87 Add new BQ AI/ML references. 技能描述扩大了适用情境，参考目录则改为函数清单，并加入分类、评估、嵌入、搜索、相似度、关键驱动因素与矢量搜索等 AI/ML 功能指南。 1c3a246 Add AI.KEY_DRIVERS to bigquery-ai-ml 技能描述与概述将 AI.KEY_DRIVERS 纳入所述功能，参考目录也添加 AI Key Drivers 项目。 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 技能的前置中继数据添加一个区块，其中包含 AiAndMachineLearning 类别。 872e31b Refactor BQ skills 此变更创建 BigQuery AI & ML 技能文件，加入描述、预测、异常侦测与文字生成的参考资料，以及 BigQuery Basics 技能的链接。 npx skills add "lettucebo/Skills#v2.2.0@bigquery-ai-ml" --full-depth ⚠ v2.2.0 发布后即可使用
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.