June 13th, 2018

How to Frame Your Business Problem for Automatic Machine Learning

Category: Data Science, Driverless AI, Machine Learning, Uncategorized
Fallback Featured Image

Over the last several years, machine learning has become an integral part of many organizations’ decision-making at various levels. With not enough data scientists to fill the increasing demand for data-driven business processes, H2O.ai has developed a product called Driverless AI that automates several time consuming aspects of a typical data science workflow: data visualization, feature engineering, predictive modeling, and model explanation. In this post, I will describe Driverless AI, how you can properly frame your business problem to get the most out of this automatic machine learning product, and how automatic machine learning is used to create business value.

What is Driverless AI and what kind of business problems does it solve?

H2O Driverless AI is a high-performance, GPU-enabled computing platform for automatic development and rapid deployment of state-of-the-art predictive analytics models. It reads tabular data from plain text sources, Hadoop, or S3 buckets and automates data visualization and building predictive models. Driverless AI is currently targeting business applications like loss-given-default, probability of default, customer churn, campaign response, fraud detection, anti-money-laundering, demand forecasting, and predictive asset maintenance models. (Or in machine learning parlance: common regression, binomial classification, and multinomial classification problems.)

How do you frame business problems in a data set for Driverless AI?

The data that is read into Driverless AI must contain one entity per row, like a customer, patient, piece of equipment, or financial transaction. That row must also contain information about what you will be trying to predict using similar data in the future, like whether that customer in the row of data used a promotion, whether that patient was readmitted to the hospital within thirty days of being released, whether that piece of equipment required maintenance, or whether that financial transaction was fraudulent. (In data science speak, Driverless AI requires “labeled” data.) Driverless AI runs through your data many, many times looking for interactions, insights, and business drivers of the phenomenon described by the provided data set. Driverless AI can handle simple data quality problems, but it currently requires all data for a single predictive model to be in the same data set and that data set must have already undergone standard ETL, cleaning, and normalization routines before being loaded into Driverless AI.

How do you use Driverless AI results to create commercial value?

Commercial value is generated by Driverless AI in a few ways.

  • Driverless AI empowers data scientists or data analysts to work on projects faster and more efficiently by using automation and state-of-the-art computing power to accomplish tasks in just minutes or hours that can take humans months.
  • Like in many other industries, automation leads to standardization of business processes, enforces best practices, and eventually drives down the cost of delivering the final product – in this case a predictive model.
  • Driverless AI makes deploying predictive models easy – typically a difficult step in the data science process. In large organizations, value from predictive modeling is typically realized when a predictive model is moved from a data analysts’ or data scientists’ development environment into a production deployment setting where the model is running on live data, making decisions quickly and automatically that make or save money. Driverless AI provides both Java- and Python-based technologies to make production deployment simpler.

Moreover, the system was designed with interpretability and transparency in mind. Every prediction made by a Driverless AI model can be explained to business users, so the system is viable even for regulated industries.

Customer success stories with Driverless AI

PayPal tried Driverless AI on a collusion fraud use case and found that simply running on a laptop for 2 hours, Driverless AI yielded impressive fraud detection accuracy, and running on GPU-enhanced hardware, it was able to produce the same accuracy in just 20 minutes. The Driverless AI model was more accurate than PayPal’s existing predictive model and the Driverless AI system found the same insights in their data that their data scientists did! The system also found new features in their data that had not been used before for predictive modeling. For more information about the PayPal use case, click here
G5, a real estate marketing optimization firm, uses Driverless AI in their Intelligent Marketing Cloud to assist clients in targeted marketing spending for property management. Empowered by Driverless AI technology, marketers can quickly prioritize and convert highly qualified inbound leads from G5’s Intelligent Marketing Cloud platform with 95 percent accuracy for serious purchase intent. To learn more about how G5 uses Driverless AI check out:

https://www.h2o.ai/g5-h2o-ai-partner-to-deliver-ai-optimization-for-real-estate-marketing/

How can you try Driverless AI?

Visit: https://www.h2o.ai/driverless-ai/ and download your free 21-day evaluation copy.

We are happy to help you get started installing and using Driverless AI, and here are some resources we’ve put together to enable in that process:

Leave a Reply

The Journey of Pi and AI: An AI conference with heart

I was in San Francisco this (past) week as part of H2O World 2019. I

February 8, 2019 - by Thomas Ott
Key Takeaways from the Gartner Magic Quadrant For Data Science & Machine Learning

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Jan 2019) is out

January 30, 2019 - by
Fallback Featured Image
What is Your AI Thinking? Part 2

Explaining AI to the Business Person Welcome to part 2 of our blog series: What is

January 24, 2019 - by Patrick Hall
H2O New Year releases

There were two releases shortly after each other. First, on December 21st, there was a

January 18, 2019 - by Pavel Pscheidl
What is Your AI Thinking? Part 1

Explaining AI to the Business Person Explainable AI is in the news, and for good reason.

January 17, 2019 - by Patrick Hall
Finally, You Can Plot H2O Decision Trees in R

Creating and plotting decision trees (like one below) for the models created in H2O will

January 15, 2019 - by Gregory Kanevsky

Join the AI Revolution

Subscribe, read the documentation, download or contact us.

Subscribe to the Newsletter

Start Your 21-Day Free Trial Today

Get It Now
Desktop img