[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M698ZEVMRDDFX2MH3GHQGSNW":3,"skill-same:01M698ZEVMRDDFX2MH3GHQGSNW":164},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M698ZEVMRDDFX2MH3GHQGSNW","bigquery-ai-ml",null,"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.","google\u002Fadk-python","google","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1342004?v=4","01M698VYEFDM0VJBZDYYJ2T83S","Google","01M698XZC2HCATT8VD5VAVZDTP",22178,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fadk-python\u002Fblob\u002Fmain\u002Fsrc\u002Fgoogle\u002Fadk\u002Fintegrations\u002Fbigquery\u002Fskills\u002Fbigquery-ai-ml\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,9,39,40,41,42,43,44,45],"bigquery ai ml","ml","bigquery","ai ml","search","when","generative","you","advanced","dataset","leverage","structural","time","effect","vector","capabilities","components","translate","generative ai","adk","genai","summarize","2026-11-10 10:12:30",false,"---\nname: bigquery-ai-ml\nlicense: Apache-2.0\nmetadata:\n  author: google-adk\n  version: \"1.0\"\ndescription: >-\n  Leverages BigQuery's built-in machine learning and GenAI capabilities\n  for advanced data analytics. Use when you need to write SQL queries\n  that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers,\n  perform semantic search or vector search, classify text, calculate similarity,\n  summarize content, translate language, evaluate models, filter by semantic conditions,\n  measure the causal effect of an intervention, compute correlations between columns,\n  detect change points or structural breaks, extract trend or seasonality components,\n  or leverage generative AI capabilities in BigQuery. Do not use for general\n  BigQuery dataset, table, or job management requests.\n---\n\n# BigQuery AI & ML\n\nBigQuery integrates with Vertex AI to provide powerful machine learning and\ngenerative AI capabilities directly within SQL queries using built-in functions\nlike `AI.FORECAST`, `AI.KEY_DRIVERS`, `AI.DETECT_ANOMALIES`, and `AI.GENERATE`.\n\n## Reference Directory\n\n-   **Functions Reference**:\n\n    -   **AI.AGG**: [ai_agg.md](references\u002Fai_agg.md) - Multi-row semantic\n        aggregation and summarization.\n    -   **AI.CAUSAL_EFFECT**:\n        [ai_causal_effect.md](references\u002Fai_causal_effect.md) - Quantifies the\n        impact of an intervention on a time series.\n    -   **AI.CLASSIFY**: [ai_classify.md](references\u002Fai_classify.md) - Classify\n        text.\n    -   **AI.DETECT_ANOMALIES**:\n        [ai_detect_anomalies.md](references\u002Fai_detect_anomalies.md) - Detect\n        anomalies.\n    -   **AI.EVALUATE**: [ai_evaluate.md](references\u002Fai_evaluate.md) - Evaluate\n        models.\n    -   **AI.FORECAST**: [ai_forecast.md](references\u002Fai_forecast.md) -\n        Time-series forecasting.\n    -   **AI.GENERATE**: [ai_generate.md](references\u002Fai_generate.md) - Generate\n        text using LLMs.\n    -   **AI.GENERATE_EMBEDDING**:\n        [ai_generate_embedding.md](references\u002Fai_generate_embedding.md) -\n        Generate embeddings.\n    -   **AI.GENERATE_TABLE**:\n        [ai_generate_table.md](references\u002Fai_generate_table.md) - Table-valued\n        AI generation.\n    -   **AI.IF**: [ai_if.md](references\u002Fai_if.md) - Evaluate semantic\n        conditions.\n    -   **AI.KEY_DRIVERS**: [ai_key_drivers.md](references\u002Fai_key_drivers.md) -\n        Identifies key drivers, this is a TVF.\n    -   **AI.SCORE**: [ai_score.md](references\u002Fai_score.md) - Score data.\n    -   **AI.SEARCH**: [ai_search.md](references\u002Fai_search.md) - Semantic\n        search.\n    -   **AI.SIMILARITY**: [ai_similarity.md](references\u002Fai_similarity.md) -\n        Semantic similarity.\n    -   **Remote Models**: [remote_models.md](references\u002Fremote_models.md) -\n        Working with remote models (Vertex AI).\n    -   **CONTRIBUTION_ANALYSIS**:\n        [ml_contribution_analysis.md](references\u002Fml_contribution_analysis.md)\n        -   Finds contributing factors, key drivers of change. Requires creating\n            a MODEL entity.\n    -   **ML.CORRELATION**: [ml_correlation.md](references\u002Fml_correlation.md) -\n        Calculates correlation between columns, optionally sliced by dimensions.\n    -   **ML.DETECT_CHANGE_POINTS**:\n        [ml_detect_change_points.md](references\u002Fml_detect_change_points.md) -\n        Detects structural breaks or sustained shifts in a time series.\n    -   **ML.SEASONALITY**: [ml_seasonality.md](references\u002Fml_seasonality.md) -\n        Extracts seasonal components from a time series.\n    -   **ML.TREND**: [ml_trend.md](references\u002Fml_trend.md) - Extracts the\n        long-term trend component from a time series.\n    -   **VECTOR_SEARCH**: [vector_search.md](references\u002Fvector_search.md) -\n        Vector search best practices.\n","# BigQuery AI & ML\n\nBigQuery integrates with Vertex AI to provide powerful machine learning and\ngenerative AI capabilities directly within SQL queries using built-in functions\nlike `AI.FORECAST`, `AI.KEY_DRIVERS`, `AI.DETECT_ANOMALIES`, and `AI.GENERATE`.\n\n## Reference Directory\n\n-   **Functions Reference**:\n\n    -   **AI.AGG**: [ai_agg.md](references\u002Fai_agg.md) - Multi-row semantic\n        aggregation and summarization.\n    -   **AI.CAUSAL_EFFECT**:\n        [ai_causal_effect.md](references\u002Fai_causal_effect.md) - Quantifies the\n        impact of an intervention on a time series.\n    -   **AI.CLASSIFY**: [ai_classify.md](references\u002Fai_classify.md) - Classify\n        text.\n    -   **AI.DETECT_ANOMALIES**:\n        [ai_detect_anomalies.md](references\u002Fai_detect_anomalies.md) - Detect\n        anomalies.\n    -   **AI.EVALUATE**: [ai_evaluate.md](references\u002Fai_evaluate.md) - Evaluate\n        models.\n    -   **AI.FORECAST**: [ai_forecast.md](references\u002Fai_forecast.md) -\n        Time-series forecasting.\n    -   **AI.GENERATE**: [ai_generate.md](references\u002Fai_generate.md) - Generate\n        text using LLMs.\n    -   **AI.GENERATE_EMBEDDING**:\n        [ai_generate_embedding.md](references\u002Fai_generate_embedding.md) -\n        Generate embeddings.\n    -   **AI.GENERATE_TABLE**:\n        [ai_generate_table.md](references\u002Fai_generate_table.md) - Table-valued\n        AI generation.\n    -   **AI.IF**: [ai_if.md](references\u002Fai_if.md) - Evaluate semantic\n        conditions.\n    -   **AI.KEY_DRIVERS**: [ai_key_drivers.md](references\u002Fai_key_drivers.md) -\n        Identifies key drivers, this is a TVF.\n    -   **AI.SCORE**: [ai_score.md](references\u002Fai_score.md) - Score data.\n    -   **AI.SEARCH**: [ai_search.md](references\u002Fai_search.md) - Semantic\n        search.\n    -   **AI.SIMILARITY**: [ai_similarity.md](references\u002Fai_similarity.md) -\n        Semantic similarity.\n    -   **Remote Models**: [remote_models.md](references\u002Fremote_models.md) -\n        Working with remote models (Vertex AI).\n    -   **CONTRIBUTION_ANALYSIS**:\n        [ml_contribution_analysis.md](references\u002Fml_contribution_analysis.md)\n        -   Finds contributing factors, key drivers of change. Requires creating\n            a MODEL entity.\n    -   **ML.CORRELATION**: [ml_correlation.md](references\u002Fml_correlation.md) -\n        Calculates correlation between columns, optionally sliced by dimensions.\n    -   **ML.DETECT_CHANGE_POINTS**:\n        [ml_detect_change_points.md](references\u002Fml_detect_change_points.md) -\n        Detects structural breaks or sustained shifts in a time series.\n    -   **ML.SEASONALITY**: [ml_seasonality.md](references\u002Fml_seasonality.md) -\n        Extracts seasonal components from a time series.\n    -   **ML.TREND**: [ml_trend.md](references\u002Fml_trend.md) - Extracts the\n        long-term trend component from a time series.\n    -   **VECTOR_SEARCH**: [vector_search.md](references\u002Fvector_search.md) -\n        Vector search best 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