Why Driverless AI?¶
Over the last several years, machine learning has become an integral part of many organizations’ decision-making processes at various levels. With not enough data scientists to fill the increasing demand for data-driven business processes, H2O.ai offers Driverless AI, which automates several time consuming aspects of a typical data science workflow, including data visualization, feature engineering, predictive modeling, and model explanation.
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 and from a variety of external data sources, and it automates data visualization and the construction of predictive models.
Driverless AI also includes robust Machine Learning Interpretability (MLI), which incorporates a number of contemporary approaches to increase the transparency and accountability of complex models by providing model results in a human-readable format.
Driverless AI targets business applications such as 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.)
Visit https://www.h2o.ai/driverless-ai/ to download your free 21-day evaluation copy.
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 dataset. Driverless AI can handle simple data quality problems, but it currently requires all data for a single predictive model to be in the same dataset, and that dataset 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 instead of the weeks or months that it can take humans.
- 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 analyst’s or data scientist’s development environment into a production deployment setting. In this setting, the model is running on live data and making quick and automatic decisions 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.