From discovering new insights from multimodal data to personalizing customer experiences, AI is emerging as the engine of modern innovation. The explosion in AI adoption has created a need to bring data and AI closer together. This is not only to streamline the AI lifecycle, but also to deliver AI-driven insights and workflow automation to everyone in your organization.
Google created BigQuery ML to bring AI to your data. This enables data scientists and data analysts to build and deploy machine learning models directly within BigQuery. Over the years, we’ve built on this foundation by introducing capabilities such as AI-powered search, generative AI using SQL, and more.
Today we will introduce: BigQuery A.I.integrates BigQuery’s built-in ML capabilities, generative AI capabilities, vector search, intelligent agents, and agent tools. With BigQuery AI, you can:
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Apply generative AI to your data. Ingest AI models from Google and partners directly into your multimodal data in BigQuery through simple SQL functions.
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Simplify the transition from data to ML. Manage the entire machine learning lifecycle with BigQuery. From feature engineering to model training, tuning, inference, and monitoring, you can manage everything without moving your data.
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Create workflows and apps faster. Whether you’re a data engineer, data scientist, or business user, speed up your workflows with intuitive controls tailored to your role. agent Built directly into BigQuery.
Let’s take a closer look at the tools and technologies that fall under the BigQuery AI umbrella.
Unlock insights from multimodal data using generative AI
Deploying state-of-the-art AI models directly into your data through simple SQL commands not only helps you perform generative AI tasks, but also allows you to derive deeper semantic understanding from your multimodal data.
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AI function Integrate LLM and embedded models directly into SQL queries to perform tasks such as content generation, analysis, summarization, structured data extraction, classification, embedding generation, and data enrichment. You can also use AI capabilities for everyday tasks such as filtering, rating, and classification. and Managed AI capabilitiesBigQuery chooses the model that is optimized for cost and quality.
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Embed and search function Find information more intelligently. While traditional text search lets you quickly find specific keywords in your data, vector search allows you to search by meaning and context, not just exact words. This allows you to uncover conceptually related items and find relevant information that a simple keyword search might miss. BigQuery’s embeddings and vector search are powerful for use cases such as RAGs, multimodal search, data deduplication, clustering, and recommendation engines.
From data processing to AI inference, all under one roof
When we first released BigQuery ML, our goal was to bring AI and ML closer to the data, allowing SQL users to perform machine learning tasks directly in BigQuery on BigQuery data. Over the years, we have added capabilities that provide a complete end-to-end platform to accelerate the entire machine learning lifecycle.
Businesses are leveraging these capabilities to powerful effect. for example, puma used BigQuery’s integrated machine learning capabilities to go beyond manual segmentation to create sophisticated audience segments based on purchasing trends. The results had a huge impact. Top ML-derived audience segments saw a 149.8% increase in click-through rates, a 4.6% increase in conversion rates, and a 6% increase in average order value.
