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, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
Why we recommend it: Leverages BigQuery built-in ML and GenAI SQL functions for forecasting, anomaly detection, key drivers, classification, semantic search, embeddings, and model evaluation.
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SKILL.md
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---
name: bigquery-ai-ml
metadata:
category: AiAndMachineLearning
description: >-
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,
or leverage generative AI capabilities in BigQuery. Do not use for general
BigQuery dataset, table, or job management requests.
---
# 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](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.
## Related Skills
- [BigQuery Basics Skill](../bigquery-basics): SKILL.md file for core BigQuery
concepts, resource management, CLI, and client libraries.
Read the full source on GitHub (opens external page)