Training Gradient Boosted Tree Models with Azure ABS Data

Azure ABS

Azure Data Lake Storage is primarily designed to work with Hadoop and all frameworks that use the Hadoop FileSystem as their data access layer (for example, Spark and Presto). It is a massively scalable, secure data lake functionality built on Azure Blob Storage which is designed for big data analytics and offers a hierarchical file system.

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 Azure ABS 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 Azure ABS. Kaspian offers native connectors for this operation. Just register your Azure ABS 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.