The need to use AI in our analyses

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In today's world, where information is generated at a canada phone number list dizzying pace, we need tools that allow us to efficiently analyze and understand it. Generative AI is a breath of fresh air, capable of automating tasks that previously required hours of manual labor and uncovering hidden patterns in large volumes of data.

BigQuery ML, Google Cloud's machine learning and AI tool, lets you access generative AI models like Gemini and Claude directly from BigQuery. This means you can apply the power of AI to your data without leaving your analytics environment, without having to program in Python or JavaScript, or configure servers or APIs. Everything is done with the SQL language you already know, applied to the world of generative AI.

With BigQuery ML and its GENERATE_TEXT function , you can:

Automatically generate content: product descriptions, blog posts, text summaries, etc.
Analyze sentiment and intent in customer feedback.
Create analyses that answer questions naturally and accurately.
Autocluster content and create new variables in your data.
Translate, synthesize and homogenize texts in bulk.
And much more…
In this post, we'll guide you step-by-step to get started using BigQuery ML and generative AI in your own projects. You'll learn how to create models, generate text, and apply this technology to real-world use cases in your business. Get ready for a data revolution!

What is BigQuery ML?
BigQuery ML is a module that we all already have active in BigQuery and that allows you to create and run machine learning and AI models directly in BigQuery SQL. This is a huge advantage, as you don't need to export your data to other platforms or learn complex programming languages ​​like Python or R. You can use SQL to work with BigQuery, train, and use machine learning models. This makes it easy to integrate machine learning into your data analysis workflows.

You can learn more about this in our BigQuery ML introduction post .

One of the great advantages of BigQuery ML is its ability to connect with Vertex AI, Google Cloud's machine learning platform. Vertex AI gives you access to a wide range of pre-trained models, including classification, regression, time series prediction, and more. Best of all, thanks to the integration with BigQuery ML, you can use them directly from BigQuery, without having to worry about infrastructure or model management.

In this post, we'll focus on the advanced generative AI language models available in Vertex AI, such as Gemini 2.0, Gemini 1.5 (Pro and Flash versions), Claude Sonnet, and Claude Haiku. These models are capable of generating text, translating languages, writing different types of creative content, and answering your questions informatively. And thanks to BigQuery ML, you can access their full potential with simple SQL queries.

The GENERATE_TEXT function is the key to automated content generation in BigQuery ML. This function allows you to send a prompt (an instruction or question) to a language model and receive a text response. You can use GENERATE_TEXT to generate all types of content, from product descriptions to blog posts, text summaries, translations, and more. All without leaving BigQuery and with the convenience of using SQL.