End-to-end user journeys for generative AI models

This document describes the user journeys for BigQuery ML remote models, including the statements and functions that you can use to work with remote models. BigQuery ML offers the following types of remote models:

Remote model user journeys

The following table describes the statements and functions you can use to create, evaluate, and generate data from remote models:

Model category

Model type

Model creation

Evaluation

Inference

Tutorials

Generative AI remote models

Remote model over a Gemini text generation model ¹

CREATE MODEL

ML.EVALUATE

Remote model over a partner text generation model

CREATE MODEL

ML.EVALUATE

ML.GENERATE_TEXT

N/A

Remote model over an open text generation model ³

CREATE MODEL

ML.EVALUATE

ML.GENERATE_TEXT

Generate text with Gemma and public data

Remote model over a Google embedding generation model

CREATE MODEL

N/A

ML.GENERATE_EMBEDDING

Remote model over an open embedding generation model ³

CREATE MODEL

N/A

ML.GENERATE_EMBEDDING

Generate text embeddings by using an open model and the ML.GENERATE_EMBEDDING function

Cloud AI remote models

Remote model over the Cloud Vision API

CREATE MODEL

N/A

ML.ANNOTATE_IMAGE

Annotate images

Remote model over the Cloud Translation API

CREATE MODEL

N/A

ML.TRANSLATE

Translate text

Remote model over the Cloud Natural Language API

CREATE MODEL

N/A

ML.UNDERSTAND_TEXT

Understand text

Remote model over the Document AI API

CREATE MODEL

N/A

ML.PROCESS_DOCUMENT

Remote model over the Speech-to-Text API

CREATE MODEL

N/A

ML.TRANSCRIBE

Transcribe audio files

Remote model over a custom model deployed to Vertex AI

CREATE MODEL

ML.EVALUATE

ML.PREDICT

Make predictions with a custom model

¹ Some Gemini models support supervised tuning .

² This function calls a hosted Gemini model, and doesn't require you to create a model separately using the CREATE MODEL statement.

³ You can automatically deploy an open model when you create the BigQuery ML remote model by specifying the model's Hugging Face or Vertex AI Model Garden ID. BigQuery manages the Vertex AI resources of open models deployed in this way, and lets you interact with those Vertex AI resources by using the BigQuery ML ALTER MODEL and DROP MODEL statements. It also lets you configure automatic undeployment of the model. For more information, see Automatically deployed models . This feature is in Preview .