Training Gradient Boosted Tree Models with PostgreSQL Data

PostgreSQL

PostgreSQL (a.k.a. Postgres) is a free and open-source relational database management system that is widely used by developers. It is highly customizable and has many useful features for developers. PostgreSQL is one of the most widely-used free and open-source relational database management systems that focus primarily on SQL compliance and extensibility. PostgreSQL offers true community-driven development, extensibility, strong SQL compliance, and useful features such as table inheritance and function overloading.

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