The possibilities are endless
Posted: Sat May 24, 2025 6:29 am
Scalability: BigQuery ML is designed to handle large canada phone number list volumes of data. You can generate thousands of AI queries in a single query, allowing you to automate large-scale content generation. Forget server limitations, API timeouts, or having to launch requests one by one. With BigQuery ML, scalability is guaranteed.
Personalization: You can customize the prompts you send to language models using variables and data from your own tables. This allows you to generate content tailored to your specific needs. You can use data from your customers, your products, your marketing campaigns…
Flexibility: BigQuery ML allows you to adjust language model variables in real time, giving you complete control over the content generation process. You can adjust the model's temperature (creativity), the maximum number of output tokens, and much more. This way, you can fine-tune the model to your needs and get the results you're looking for.
Auto-analysis: BigQuery ML can automatically generate variables or filter data, helping you uncover hidden patterns in your data. Let AI do the heavy lifting for you. You can use generative AI to create new variables from your data, or to intelligently filter information.
Export and Visualization: You can export BigQuery ML results to Looker Studio, CSV, or Google Sheets for further analysis and visualization. This allows you to integrate generative AI into your existing workflows and share results with your team.
Advanced analytics with BigQuery ML
In addition to content generation, BigQuery ML lets you perform advanced text analytics, such as:
Sentiment analysis: determining whether a text expresses a positive, negative, or neutral emotion. This is very useful for analyzing your customers' opinions about your products or services.
Topic Identification: Extract the main topics from a set of texts. You can use this feature to analyze your customers' conversations or to classify your company's documents.
Text Classification: Assign text to a predefined category. You can use this feature to classify your customer emails, news articles, or any other type of text.
Text clustering: Grouping similar texts together. This allows you to discover patterns in large text datasets, such as identifying groups of customers with similar interests.
You can also use BigQuery ML to:
Create variables and filters without writing code. Generative AI can help you create complex variables or intelligently filter information, without writing SQL code.
Automate data classification and clustering. Let AI organize your data for you. You can use generative AI to classify your products, your customers, or any other type of data.
Translate texts in bulk. Translate large volumes of text quickly and easily, without the need for external services.
Generate campaign performance reports. Automate reporting for your marketing campaigns, including text analysis and summary generation.
And much more… The possibilities are endless. Generative AI with BigQuery ML opens up a world of possibilities. Explore and discover everything you can do!
And it can go even further. In future posts (not this one), we'll explore advanced BigQuery ML features, such as model fine-tuning, which allows you to adapt pre-trained models to your specific needs. This allows you to get even greater performance from generative AI. But right now, our goal is to learn how to use generic AI.
Personalization: You can customize the prompts you send to language models using variables and data from your own tables. This allows you to generate content tailored to your specific needs. You can use data from your customers, your products, your marketing campaigns…
Flexibility: BigQuery ML allows you to adjust language model variables in real time, giving you complete control over the content generation process. You can adjust the model's temperature (creativity), the maximum number of output tokens, and much more. This way, you can fine-tune the model to your needs and get the results you're looking for.
Auto-analysis: BigQuery ML can automatically generate variables or filter data, helping you uncover hidden patterns in your data. Let AI do the heavy lifting for you. You can use generative AI to create new variables from your data, or to intelligently filter information.
Export and Visualization: You can export BigQuery ML results to Looker Studio, CSV, or Google Sheets for further analysis and visualization. This allows you to integrate generative AI into your existing workflows and share results with your team.
Advanced analytics with BigQuery ML
In addition to content generation, BigQuery ML lets you perform advanced text analytics, such as:
Sentiment analysis: determining whether a text expresses a positive, negative, or neutral emotion. This is very useful for analyzing your customers' opinions about your products or services.
Topic Identification: Extract the main topics from a set of texts. You can use this feature to analyze your customers' conversations or to classify your company's documents.
Text Classification: Assign text to a predefined category. You can use this feature to classify your customer emails, news articles, or any other type of text.
Text clustering: Grouping similar texts together. This allows you to discover patterns in large text datasets, such as identifying groups of customers with similar interests.
You can also use BigQuery ML to:
Create variables and filters without writing code. Generative AI can help you create complex variables or intelligently filter information, without writing SQL code.
Automate data classification and clustering. Let AI organize your data for you. You can use generative AI to classify your products, your customers, or any other type of data.
Translate texts in bulk. Translate large volumes of text quickly and easily, without the need for external services.
Generate campaign performance reports. Automate reporting for your marketing campaigns, including text analysis and summary generation.
And much more… The possibilities are endless. Generative AI with BigQuery ML opens up a world of possibilities. Explore and discover everything you can do!
And it can go even further. In future posts (not this one), we'll explore advanced BigQuery ML features, such as model fine-tuning, which allows you to adapt pre-trained models to your specific needs. This allows you to get even greater performance from generative AI. But right now, our goal is to learn how to use generic AI.