Customers Praise H2O.ai’s Feature Engineering, Model Explainability, and Implementation Support
H2O.ai, the open source leader in AI and Machine Learning, today announced it has been named a Leader in “The Forrester New Wave: Automation-Focused Machine Learning Solutions, Q2 2019” report by Forrester Research, Inc. Among the nine vendors evaluated, H2O.ai received differentiated ratings in the User Experience, Data Capabilities, Feature Engineering, Model Training, Evaluation and Vision Criteria.
Forrester notes that H2O.ai “is best for companies that want to delight data science teams.” The report explains, “Enterprises struggle to expand data science teams because data scientists are hard to hire. The solution: Make existing teams more productive with H2O.ai’s AutoML solution.”
In its evaluation, Forrester reports that H2O.ai’s Driverless AI “Leads with highly transparent, tunable automation,” and continues to state that Driverless AI “is designed for data scientists first and foremost by offering a rich set of configurations to control how the automation works.”
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“We are excited and humbled in Forrester’s recognition of H2O Driverless AI as leader in Forrester Wave for Automation-Focused Machine Learning Solutions. H2O Driverless AI delights data scientists evidenced by an exponential growth in adoption and nearly perfect renewal rates this year. Customers of Driverless AI are deploying and benchmarking models with orders of magnitude ease and efficiency and processing thousands of features at a pace impossible to do without H2O. AutoML is making AI accessible to enterprises bridging the talent gap and bringing trust and consistency to machine learning. Driverless AI’s next generation Bring Your Own Recipe architecture will make AutoML fully extensible and customizable by domain scientists and strategists in the field and democratize AI further,” said Sri Ambati, CEO and Founder of H2O.ai.
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The Forrester New Wave: Automation-Focused Machine Learning Solutions, Q2 2019 report used 10 criteria spanning product functionality and strategy to evaluate nine providers within the emerging market. The report “found that solutions developed with advanced feature engineering capabilities and model transparency were key differentiators for Leaders in the AutoML space, empowering citizen data scientists and data scientists alike to tackle more-challenging use cases.”
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