Bigquery Ai Ml

by google55b4e13eba6dNo licenseListed Oct 8, 2026Updated Oct 8, 2026

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.

FeaturedInstructions onlyData & Analytics
AI-generated overview

Guides writing BigQuery SQL that uses built-in AI and ML functions for forecasting, anomaly detection, classification, and semantic search.

What it does
This skill provides reference guidance for using BigQuery's built-in AI and ML functions inside SQL queries, including AI.FORECAST, AI.KEY_DRIVERS, AI.DETECT_ANOMALIES, AI.GENERATE, AI.CLASSIFY, AI.SEARCH, and ML.CORRELATION. It covers time-series forecasting, anomaly and change-point detection, key driver and contribution analysis, trend and seasonality extraction, text classification, semantic and vector search, embeddings, similarity, model evaluation, and causal effect measurement. The deliverable is SQL query guidance rather than generated files or scripts.
When to use it
Use it when you need to write BigQuery SQL that performs machine learning or generative AI analytics, such as forecasting, outlier detection, semantic search, or text classification. It is not intended for general BigQuery dataset, table, or job management requests.
Requirements
Requires BigQuery with access to its built-in AI and ML functions and, for generative and remote model functions, Vertex AI integration. Instructions only; no scripts are included.

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 [blocked] - Multi-row semantic aggregation and summarization.
    • AI.CAUSAL_EFFECT: ai_causal_effect.md [blocked] - Quantifies the impact of an intervention on a time series.
    • AI.CLASSIFY: ai_classify.md [blocked] - Classify text.
    • AI.DETECT_ANOMALIES: ai_detect_anomalies.md [blocked] - Detect anomalies.
    • AI.EVALUATE: ai_evaluate.md [blocked] - Evaluate models.
    • AI.FORECAST: ai_forecast.md [blocked] - Time-series forecasting.
    • AI.GENERATE: ai_generate.md [blocked] - Generate text using LLMs.
    • AI.GENERATE_EMBEDDING: ai_generate_embedding.md [blocked] - Generate embeddings.
    • AI.GENERATE_TABLE: ai_generate_table.md [blocked] - Table-valued AI generation.
    • AI.IF: ai_if.md [blocked] - Evaluate semantic conditions.
    • AI.KEY_DRIVERS: ai_key_drivers.md [blocked] - Identifies key drivers, this is a TVF.
    • AI.SCORE: ai_score.md [blocked] - Score data.
    • AI.SEARCH: ai_search.md [blocked] - Semantic search.
    • AI.SIMILARITY: ai_similarity.md [blocked] - Semantic similarity.
    • Remote Models: remote_models.md [blocked] - Working with remote models (Vertex AI).
    • CONTRIBUTION_ANALYSIS: ml_contribution_analysis.md [blocked]
      • Finds contributing factors, key drivers of change. Requires creating a MODEL entity.
    • ML.CORRELATION: ml_correlation.md [blocked] - Calculates correlation between columns, optionally sliced by dimensions.
    • ML.DETECT_CHANGE_POINTS: ml_detect_change_points.md [blocked] - Detects structural breaks or sustained shifts in a time series.
    • ML.SEASONALITY: ml_seasonality.md [blocked] - Extracts seasonal components from a time series.
    • ML.TREND: ml_trend.md [blocked] - Extracts the long-term trend component from a time series.
    • VECTOR_SEARCH: vector_search.md [blocked] - Vector search best practices.

Related Skills

  • BigQuery Basics Skill: SKILL.md file for core BigQuery concepts, resource management, CLI, and client libraries.

Source and attribution

Source:google/skillsinskills/cloud/bigquery-ai-mlat commit55b4e13

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from google/skills

Dpop Adoption

google

Featured

Implement and debug OAuth 2.0 DPoP (RFC 9449) refresh token sender-constraining for WebCrypto, Node.js ES6, and browser runtimes integrating with Google's OAuth platform. Use when configuring non-extractable asymmetric key pairs (P-256), generating DPoP Proof JWTs for authorization code exchange and token refresh, or handling 400 use_dpop_nonce challenge retry loops at oauth2.googleapis.com/token. Don't use for unconstrained OAuth 2.0 flows (where refresh tokens are not bound to a client key pair), or for Google Cloud IAM / service account authentication.

Awaiting classificationOct 8, 2026

Finding Google Skills

google

Featured

Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.

Awaiting classificationOct 8, 2026

Spanner Basics

google

Featured

Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.

Data & AnalyticsOct 8, 2026

Secops Triage

google

Featured

Guides SOC analysts through triaging Google SecOps security alerts, from investigation to closure or escalation.

SecurityOct 8, 2026

Secops Investigate

google

Featured

Guides SOC analysts through deep security incident and entity investigations in Google SecOps using UDM queries and timelines.

SecurityOct 8, 2026

Secops Hunt

google

Featured

Guides proactive threat hunting in Google SecOps using UDM queries, IoC lookback, prevalence and outlier analysis.

SecurityOct 8, 2026