Training Gradient Boosted Tree Models with data in your NoSQL Database

NoSQL Database

NoSQL databases are popular because they are highly scalable and flexible, making them ideal for handling large amounts of unstructured data. They can also be used to store structured data. NoSQL databases are designed to handle big data and can be used to store data in a variety of formats including JSON, XML, and BSON. They are also highly available and fault-tolerant. Additionally, they are designed to scale horizontally, allowing companies to add more computing power and storage as needed.

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 NoSQL databases 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 their NoSQL database. Kaspian offers native connectors for the most popular NoSQL databases. Just register your NoSQL database as a 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.