Devron is a federated data science platform that enables teams to build and train models on distributed, heterogeneous, and private data where it resides.
In this demo, we'll walk you through the model-building process and show you how Devron can be used to train a model across vertically split (different schema) datasets without moving or exposing the data.
We will follow an example scenario where we assume the role of a marketing team from a retail bank looking to better segment their customers for new and cross-selling loan opportunities
Use Case Summary:
INDUSTRY: | Banking |
OBJECTIVE: | Customer Segmentation |
BUSINESS VALUE: | Increase Revenue |
Expand Loan Base | |
Reduce Customer Acquisition Costs | |
Personalize Services & Offers |
Unlock access to valuable, previously inaccessible datasets (including securely and privately sharing data between organizations) | |
Analyze sensitive data without the risk of privacy leakage or lineage issues | |
Boost model accuracy, generalizability, and reliability | |
Accelerate time to insight by drastically reducing data engineering overhead |