We are excited to be named a Visionary in the new Gartner Magic Quadrant for Cloud AI Developer Services (Feb 2020), and have been recognized for both our completeness of vision and ability to execute in the emerging market for cloud-hosted artificial intelligence (AI) services for application developers.

This is the second Gartner MQ that has recognized H2O.ai as a Visionary in as many weeks, which we believe validates our strong position in the AI and machine learning market. If you missed the news on the 2020 Gartner Magic Quadrant for Data Science and Machine Learning, you can download the report here and read our key takeaways.

So let’s walk you through the key strengths of our automatic machine learning platforms and how they can help companies with their AI transformations in the cloud.

Automatic Machine Learning:

Built from the ground up starting with our open source platform, H2O-3, automatic machine learning (AutoML) has always been a strength of ours. With H2O Driverless AI, we’ve made it even easier for users to train and deploy new machine learning models in less time so data scientists and developers can focus more on developing more AI use cases. H2O Driverless AI provides companies with a data science platform that addresses the needs of a variety of use cases for every enterprise in every industry with automatic feature engineering, model validation, model tuning, model selection and deployment, machine learning interpretability, time-series and automatic pipeline generation for model scoring. Driverless AI also features AutoDoc, an automatic documentation capability that generates a document describing the unique experiment pipeline chosen by Driverless AI and the user settings. These reports provide insight into the training data, including any detected shifts in distribution, validation schema, model parameter tuning, feature evolution and the list of the most important features chosen during the experiment. 

Cloud Agnostic and Easy Deployment:

CloudZies (cloud agnostic) – meaning that we can work with your team where your data already lives, whether it’s a public cloud, private cloud or on-prem. Driverless AI can be deployed anywhere. With an intuitive UI, Driverless AI automates the machine learning workflow using the industry-leading open-source machine learning algorithms that are agnostic of cloud or on-prem. Enterprise Puddle, a managed service in a virtual private cloud, also enables data science teams to deploy Driverless AI efficiently and for administrators to manage environments and costs.

Easy to Deploy Models:

We like to say “train once, run anywhere.” To that end, H2O Driverless AI enables users to automatically generate MOJOs (Model Object, Optimized), for predictive model scoring. MOJOs can be deployed from the cloud to the edge in Java, Python, R and C++ runtimes, and are algorithm independent. 

AI Transformation in the Enterprise:

We pride ourselves on high-quality customer support. Our goal is to enable our customers to become AI companies themselves, and to back them up, we offer world-class support to ensure their success. We’re proud to have a team of data science experts working with our customers, including 10 percent of the world’s Kaggle Grandmasters. Because of the strength of our team, we believe we’re able to offer companies a level of expertise that only the tech giants themselves have.

Our customers are why we continue to innovate and listening to your feedback is how we’ll all succeed. We aim to continue to democratize AI and make these innovative technologies available to more and more users, and are specifically looking to address the AI needs of business users in 2020.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.



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