Training Gradient Boosted Tree Models with MongoDB Data

MongoDB

MongoDB is a source-available cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with optional schemas. MongoDB has become one of the most wanted databases in the world because it makes it easy for developers to store, manage, and retrieve data when creating applications with most programming languages. MongoDB is popular because it's easy to learn and get started. It's highly scalable (auto-sharding) and cost-effective, and it has a flexible data model.

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