How Data Analysts Can Build and Run AI Models Using BigQuery ML

In the era of AI, the role of a data analyst is evolving rapidly from analyzing complex datasets to predicting outcomes such as customer behavior or product sales. This trend is largely driven by the increasing availability of large datasets and the development of more sophisticated machine learning algorithms. Data analysts are now able to use these tools to identify patterns and trends in data that would be impossible to detect manually. However, building and using AI models can be challenging for data analysts. You often need to learn advanced coding languages such as Python, build pipelines to move data from analytics to AI tools, and be able to import ML models into analytics tools. To help accelerate the path to AI adoption, BigQuery provides built-in capabilities that allow you to create, train, and execute machine learning models using simple SQL. BigQuery ML supports a broad range of ML models, including built-in models, models trained in Vertex AI, and imported custom models. In this webinar, you will see a live demo of BigQuery ML and learn how to: • Create and use machine learning models in BigQuery ML using simple SQL • Apply Google's best-in-class AI models, such as PaLM 2, to your data right inside BigQuery • Deploy models and operationalize ML workflows without moving data from BigQuery