AI application overview

This document describes the artificial intelligence (AI) application features that BigQuery ML supports. These features let you perform AI tasks in BigQuery ML by using Cloud AI APIs. Supported tasks include the following:

• Natural language processing
• Machine translation
• Audio transcription
• Document processing
• Computer vision

You access a Cloud AI API to perform one of these functions by creating a remote model in BigQuery ML that represents the API endpoint. Once you have created a remote model over the AI resource that you want to use, you access that resource's capabilities by running a BigQuery ML function against the remote model.

This approach lets you use the capabilities of the underlying API without having to know Python or develop familiarity with API.

Workflow

You can use remote models over Vertex AI models and remote models over Cloud AI services together with BigQuery ML functions in order to accomplish complex data analysis and generative AI tasks.

The following diagram shows some typical workflows where you might use these capabilities together:

Natural language processing

You can use natural language processing to perform tasks such as classification and sentiment analysis on your data. For example, you could analyze product feedback to estimate whether customers like a particular product.

To perform natural language tasks, you can create a reference to the Cloud Natural Language API by creating a remote model and specifying CLOUD_AI_NATURAL_LANGUAGE_V1 for the REMOTE_SERVICE_TYPE value. You can then use the ML.UNDERSTAND_TEXT function to interact with that service. ML.UNDERSTAND_TEXT works with data in standard tables. All inference occurs in Vertex AI. The results are stored in BigQuery.

To learn more, try understanding text with the ML.UNDERSTAND_TEXT function.

Machine translation

You can use machine translation to translate text data into other languages. For example, translating customer feedback from an unfamiliar language into a familiar one.

To perform machine translation tasks, you can create a reference to the Cloud Translation API by creating a remote model and specifying CLOUD_AI_TRANSLATE_V3 for the REMOTE_SERVICE_TYPE value. You can then use the ML.TRANSLATE function to interact with that service. ML.TRANSLATE works with data in standard tables. All inference occurs in Vertex AI. The results are stored in BigQuery.

To learn more, try translating text with the ML.TRANSLATE function.

Audio transcription

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Note: To give feedback or request support for this feature, send an email to [e-mail].

You can use audio transcription to transcribe audio files into written text. For example, transcribing a voicemail recording into a text message.

To perform audio transcription tasks, you can create a reference to the Speech-to-Text API by creating a remote model and specifying CLOUD_AI_SPEECH_TO_TEXT_V2 for the REMOTE_SERVICE_TYPE value. You can optionally specify a recognizer to use to process the audio content. You can then use the ML.TRANSCRIBE function to transcribe audio files. ML.TRANSCRIBE works with audio files in object tables. All inference occurs in Vertex AI. The results are stored in BigQuery.

To learn more, try transcribing audio files with the ML.TRANSCRIBE function.

Document processing

Preview

This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA products and features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Note: To give feedback or request support for this feature, send an email to [e-mail].

You can use document processing to extract insights from unstructured documents. For example, extracting relevant information from invoice files so it can be input into accounting software.

To perform document processing tasks, you can create a reference to the Document AI API by creating a remote model, specifying CLOUD_AI_DOCUMENT_V1 for the REMOTE_SERVICE_TYPE value, and specifying a processor to use to process the document content. You can then use the ML.PROCESS_DOCUMENT function to process documents. ML.PROCESS_DOCUMENT works on documents in object tables. All inference occurs in Vertex AI. The results are stored in BigQuery.

To learn more, try processing documents with the ML.PROCESS_DOCUMENT function.

Computer vision

You can use computer vision to perform image analysis tasks. For example, you could analyze images to detect whether they contain faces, or to generate labels describing the objects in the image.

To perform computer vision tasks, you can create a reference to the Cloud Vision API by creating a remote model and specifying CLOUD_AI_VISION_V1 for the REMOTE_SERVICE_TYPE value. You can then use the ML.ANNOTATE_IMAGE function to annotate images by using that service. ML.ANNOTATE_IMAGE works with data in object tables. All inference occurs in Vertex AI. The results are stored in BigQuery.

To learn more, try annotating object table images with the ML.ANNOTATE_IMAGE function.

What's next

• For more information about performing inference over machine learning models, see Model inference overview.