Training Gradient Boosted Tree Models with Snowflake Data

Snowflake

Snowflake is a cloud-based data warehousing company that provides a platform for data storage, processing and analysis. It is considered cloud-agnostic, as it operates across Amazon Web Services (AWS), Microsoft Azure or Google Cloud. Snowflake delivers a platform that is fast, flexible, and user-friendly. It provides the means for not only data storage but also processing and analysis. One of the main reasons that Snowflake is gaining recognition as the top cloud data warehousing solution is thanks to its architecture, which consists of dynamic, scalable computing power with usage-based charges out-of-the-box features such as data cloning and sharing, on-the-fly scalable computing and third-party tool support.

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 Snowflake 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 Snowflake. Kaspian offers native connectors for this operation. Just register your Snowflake 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.