BigQuery ML offers several advantages over other approaches to using ML or AI with a cloud-based data warehouse:

BigQuery ML democratizes the use of ML and AI by empowering data analysts, the primary data warehouse users, to build and run models using existing business intelligence tools and spreadsheets. Predictive analytics can guide business decision-making across the organization.
You don't need to program an ML or AI solution using Python or Java. You train models and access AI resources by using SQL—a language that's familiar to data analysts.

BigQuery ML increases the speed of model development and innovation by removing the need to move data from the data warehouse. Instead, BigQuery ML brings ML to the data, which offers the following advantages:

- Reduced complexity because fewer tools are required.
- Increased speed to production because moving and formatting large amounts of data for Python-based ML frameworks isn't required to train a model in BigQuery.