top of page

AI Case Study to offer interpretable ready-to-deploy deep learning and machine learning algorithms for enterprises to enable a faster transition to AI

Some times it is difficult to decipher algorithms and understand decision making process. H2O proposes an open source AI platform which automates steps in machine learning pipeline such as feature engineering, model validation and tuning. Its platform, Driverless AI tracks model development to understand why a particular decision was taken. This is specially significant in industries with strict regulation and auditory requirements.



Software And It Services

Project Overview

"H2O’s machine learning (ML) software, Driverless AI, automates several key steps in the machine learning pipeline (such as automatically doing feature engineering and model validation and tuning). Similarly, IBM’s PowerAI is an enterprise distribution of some of the most popular open-source deep learning frameworks like Tensorflow, Keras, PyTorch and more. PowerAI enhances open-source software like TensorFlow to help make it easier to use for the enterprise with greatly improved model training times.

Together, H2O Driverless AI and IBM PowerAI provide companies with a data science platform or an “AI workbench” that addresses a broad set of use cases for machine learning and deep learning in every industry."

Reported Results

Project announced; results are not yet available. However it is expected to automate key steps in machine learning for enterprises.




Data Science


" is focused on bringing AI to businesses through software. Its flagship product is H2O, the leading open source platform that makes it easy for financial services, insurance and healthcare companies to deploy AI and deep learning to solve complex problems. More than 9,000 organizations and 80,000+ data scientists depend on H2O for critical applications like predictive maintenance and operational intelligence."



bottom of page