Training Gradient Boosted Tree Models with BigQuery Data

BigQuery

BigQuery is a cloud-based data warehousing solution that is part of the Google Cloud Platform (GCP). It is designed to handle large amounts of data and is used by businesses of all sizes. One of the reasons BigQuery is so popular is because it is fast and scalable. It can handle large amounts of data quickly and efficiently, making it ideal for businesses that need to process large amounts of data. BigQuery also has native integrations with the most popular BI tools. This means you can connect tables to Data Studio, Looker, Power BI, Tableau, and other visualization tools with a few clicks.

Gradient Boosted Tree Models

Gradient Boosted Tree (GBT) models are a type of machine learning model that are used for classification and regression problems. They work by combining multiple decision trees together to create a more accurate model. Gradient Boosted Trees are particularly useful when working with large datasets, as they can handle both numerical and categorical data. They are also known for their ability to handle missing data well. Gradient Boosted Trees have become increasingly popular due to their high accuracy rates on many different types of datasets.

With the growing popularity of both BigQuery for storage and gradient boosted tree models for AI deployments, it is unsurprising that many organizations are seeking to train gradient boosted tree models using data in BigQuery. Kaspian offers native connectors for this operation. Just register your BigQuery datastore and link your model training job; Kaspian's autoscaling compute layer makes it easy to train and deploy gradient boosted tree models using any data in your cloud with minimal setup or management.

Learn more about Kaspian and see how our flexible compute layer for the modern data cloud is already reshaping the way companies in industries like retail, manufacturing and logistics are thinking about data engineering and analytics.