# How to Setup Google BigQuery AI/ML

Setup Google CLI

{% embed url="<https://googleapis.dev/python/google-api-core/latest/auth.html>" %}

Setup BigQuery AI

{% embed url="<https://cloud.google.com/python/docs/reference/bigframes/latest>" %}

Provided that the Python environment (`.venv`) and the project directory structure are already configured, the steps below describe how to obtain the Google Service Account credentials required to enable BigQuery access.

1. Run `pip install --upgrade bigframes` to install the latest version.
2. Setup [Application default credentials](https://cloud.google.com/docs/authentication/set-up-adc-local-dev-environment) for your local development environment enviroment.
3. Create a [GCP project with the BigQuery API enabled](https://cloud.google.com/bigquery/docs/sandbox).
4. Use the `bigframes` package to query data.

Setup Vertex AI

{% embed url="<https://cloud.google.com/blog/products/data-analytics/run-open-source-llms-on-bigquery-ml>" %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://whoamimi.gitbook.io/blog/projects/readme-1/participated-competitions/bigquery-ai-building-the-future-of-data/how-to-setup-google-bigquery-ai-ml.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.