Capital One on H2O Machine Learning

Name: Capital One on H2O Machine Learning
Uploaded: 2016-01-12
Duration: 4 min
Description: Capital One data scientist Brandon Herer on why the company adopted H2O: faster iteration on terabyte-scale data, no vendor lock-in, clearer results.

H2O-3

Overview

In this customer testimonial, Capital One data scientist Brandon Herer explains why the company adopted H2O for modeling on large-scale data. He describes how H2O let Capital One iterate on terabyte-scale datasets much faster, avoid vendor lock-in through open source, and use H2O Flow to communicate modeling results clearly to executives and teams across the company.

Key takeaways

Why Capital One needed a tool that could model terabyte-scale data quickly and iteratively
How open source access reduced vendor lock-in risk and let Capital One extend H2O when needed
How in-memory processing changed model run times from "run a model and go to lunch" to near-immediate results
How H2O Flow helped Capital One communicate modeling work to executives and less technical stakeholders
Why Capital One valued H2O's transparency, including the ability to inspect the source code behind tree and network models
How Capital One used H2O across its data science org, including hosting regular H2O community meetups

Start owning your AI

One platform. Complete sovereignty. Measurable results.

Request a demo Talk to H2O