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Key Takeaways from the 2020 Gartner Magic Quadrant for Data Science and Machine Learning


By Rafael Coss | minute read | February 17, 2020

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We are named a Visionary in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Feb 2020).  We have been positioned furthest to the right for completeness of vision among all the vendors evaluated in the quadrant. 

So let’s walk you through the key strengths of our machine learning platforms. 

Automatic Machine Learning

With a mission to democratize AI, automatic machine learning (AutoML) is a key strength of ours and helps us empower every company to become an AI company. H2O Driverless AI is the leading automatic machine learning platform that is ideal to scale a company’s data science efforts, whereas H2O AutoML is an open source project for “hands-on” data scientists. We continue to drive new innovations like automatic time series, NLP, visualization, explainable AI and extensibility of the Driverless AI platform with “ Recipes ” that empower companies to make their own AI. 

Explainability: has a long history and commitment to machine learning explainability and has some of the best minds working on these capabilities: Patrick Hall and Navdeep Gill who co-authored the book, An Introduction to Machine Learning Interpretability . Our team at has added explainability techniques and capabilities to H2O Driverless AI, so it not only automates the model building process but also the explanation of those models. This is why we assert that we have set the example for the industry-leading explainability capability. Driverless AI supports diverse techniques such as K-LIME, LIME-SUP, Shapley, variable importance, decision tree surrogate, ICE, partial dependence plots, disparate impact analysis and “what-if analysis.” Additionally, our customers rave about our AutoDoc capability that automatically generates a complete report of the modeling building process and explanations in an easy-to-edit document format. 

High-performance Machine Learning:’s strength has always been our machine learning components including high performance, scalable and easy-to-use algorithms. H2O-3 is effectively an industry standard and its algorithms and ML components are used by many platforms and vendors both in the industry and in the quadrant itself including Alteryx, Dataiku, Domino, DataRobot, IBM, KNIME, RapidMiner, TIBCO Software and others. We are proud of the fact that our platforms run in every major cloud and on-prem.   

At, we’ve always believed that our H2O-3 open source platform enables the entire ecosystem – all companies and individuals – to effectively build highly accurate models at speed and scale. 

Right from our earliest days where we were the first ones to offer fully parallelized high-performance ML algorithms to our investment in GPU accelerated machine learning, we’ve always been forward-looking in our vision and roadmap.  

Feedback is Gold

We would like to recognize our customers as key drivers that spur on our desire to innovate. We not only offer Kaggle Grandmasters and expert data science support to our customers, but also have a thriving community of over 180,000 Meetup members. We’ve held twice-a-year H2O Worlds with thousands of participants, and we have a community slack channel for immediate assistance. This is where we thrive. We listen to our customers and the community and innovate at breakneck speed with 16 Driverless AI releases last year alone. We are also working to deliver better interoperability between H2O-3 and Driverless AI. Here are some of the ways that can be achieved today: 

  • Driverless AI can build models using H2O-3 and these recipes can be found in the open catalog.
  • Driverless AI models (MOJO) can be retrained on Spark to run on big data
  • H2O-3 models can be interpreted using Machine Learning Interpretability (MLI)
  • Customers are already stitching together H2O-3 + Driverless AI  models in production today.

Finally, we continue to invest in data access and data prep capabilities through our integrations with our partners, new features in Driverless AI and the use of DataTable. With these, fast data prep is achievable. In addition,  this last year we added the capability to leverage data recipes that allow data scientists to add a Python pipeline to prepare the data. 

Makers Gonna Make

We want to thank all the Makers at, our customers and our community for helping us achieve this awesome visionary position. We continue to strive to make machine learning faster, cheaper and easier so that every company can be an AI company.  

So what’s next for you? I’ve been in the data and analytics space for 20 years and am very proud of our technology. I work with customers building solutions and hear about their outcomes. We make it easy for you to get started with Driverless AI. Try it yourself and see the results.   

A full copy of the report can be obtained here 

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, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 

Gartner Magic Quadrant for Data Science and Machine Learning Platforms, Peter Krensky, Pieter den Hamer, Erick Brethenoux, Jim Hare, Carlie Idoine, Alexander Linden, Svetlana Sicular, Farhan Choudhary, 11 February 2020 


Rafael Coss

Rafael Coss is a Community and Partner Maker at Prior to joining, he was technical marketing and community Director and a developer advocate at Hortonworks. He was also the DataWorks Summit Program Co-Chair for the past 3 years. Prior to Hortonworks he was a Senior Solution Architect and Manager of IBM’s WW Big Data Enablement team. At IBM he was responsible for the technical product enablement for BigInsights and Streams. Previously, he held several other positions in IBM, where he worked on tools, XML db, federated db and Object-Relational db.