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 and H2O.ai has been named a Visionary. The Gartner MQ evaluates platforms that enable expert data scientists, citizen data scientists and application developers to create, deploy and manage their own advanced analytic models.

H2O.ai Key Highlights:

Some of the highlights below are why we believe Gartner has recognized us in their latest Magic Quadrant report.

Strong Machine Learning Foundation & Ecosystem:

H2O’s strength has always been our Machine Learning components including high performant, scalable and easy-to-use algorithms. H2O is effectively an industry standard and it’s algorithms and ML components are used by many platforms and vendors in the industry (including Alteryx, Dataiku, Domino, Datarobot, IBM, KNIME, RapidMiner, TIBCO Software, and others).

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

Innovation & Vision:

We would like to recognize our customers as key drivers that spur on our desire to innovate.  Right from our earliest days where we were the earliest ones to offer fully parallelized high-performance ML algorithms to our investment in GPU accelerated machine learning, we’ve always forward-looking in our vision and roadmap. Additional innovations this year include the addition and optimization of algorithms (XGBoost, Isolation Forest, LightGBM, TensorFlow, etc), the addition of Time Series recipes, the pioneering and leader of explainable AI with machine learning interpretability (MLI), Auto Visualization, Auto Documentation, NLP, and the automatic pipelines generations in both Java and Python.

Automation:

We agree that automation is a key strength of ours as we are on a journey to democratize AI. 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. But we’re also investing in new innovations like automatic time series, NLP, visualization and explainable AI.

Feedback is Gold:

In addition to the key innovations that we are known for, our customers have asked for some additional capabilities in our platforms. One that we are working on is better interoperability between H2O-3 and Driverless AI. Here are some of the ways that can be achieved today:

• Driverless AI models (MOJO) can be retrained on Spark to run on big data
• H2O models can be interpreted using Machine Learning Interpretability (MLI)
• Auto Visualization (Auto Vis) and MLI use H2O as a backend for all the analysis
• Customers, such as G5, are already stitching together H2O + DAI models in production today.

We also continue to invest in data access and data prep capabilities through our integrations with our partners in addition to additional features in Driverless AI. We are adding project management feature in the latest release of Driverless AI and are moving towards additional model management capabilities through the rest of the year.

A full copy of the report can be obtained here.

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