H2O.ai

H2O.ai by sennchi

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Predictive Analytics And Machine Learning Solutions

H2O.ai puts algorithms first. H2o.ai is best known for developing open source, cluster-distributed machine learning algorithms at a time (2011) when big data demanded them but no one else had them. to say H2o.ai is an algorithm company today is an understatement. it also offers sparkling Water to create, manage, and run workflows on Apache spark and steam to deploy models. Further, it offers Flow — a notebook-like experience similar to Jupyter. the company recently announced Deep Water and amalgamated distribution of open source deep-learning libraries
caffe, MXnet, and tensorFlow. H2o.ai has seen significant adoption by large enterprises including capital one, comcast, and Macy’s. However, H2o.ai does not often displace heavies such as sAs because it only recently added a data science workbench and lacks enterprise model management capabilities. the company’s operations and growth are funded by series B venture capital.

H2O.ai , which is based in Mountain View, California, U.S., offers an open-source machine-learning platform. For this Magic Quadrant, we evaluated H2O Flow, its core component; H2O Steam; H2O Sparkling Water, for Spark integration; and H2O Deep Water, which provides deep-learning capabilities.

H2O.ai has progressed from Visionary in the prior Magic Quadrant to Leader. It continues to progress through significant commercial expansion, and has strengthened its position as a thought leader and an innovator.

STRENGTHS

• **Technology leader: **H2O.ai scored highly in categories such as deep-learning capability, automation capability, hybrid cloud support ("deploy anywhere") and open-source integration. H2O Deep Water offers a deep learning front end that abstracts away many of the details of back ends like TensorFlow, MXNet and Caffe. Its machine-learning automation capabilities (dubbed Driverless AI) are impressive and, although still developing, demonstrate the company's distinguished vision. In terms of flexibility and scalability, reference customers considered H2O.ai to be first class. It has one of the best Spark integrations, and is ahead of all the other Magic Quadrant vendors in its graphics processing unit integration efforts.

• **Mind share, partners and status as quasi-industry standard: **H2O.ai's platform is now used by almost 100,000 data scientists, and many partners (such as Angoss, IBM, Intel, RapidMiner and TIBCO Software) have integrated H2O.ai's technology platform and library. This shows the company's technical leadership, which especially derives from the highly scalable implementation of some core algorithms.

• **Customer satisfaction: **H2O.ai's reference customers gave it the highest overall score for sales relationship and account management, evaluation and contract negotiation experience, customer support (including onboarding and troubleshooting), and overall service and support. They also gave it outstanding scores for analytic support (including training and technique selection), integration and deployment, inclusion of requested product enhancements in subsequent product releases, and overall experience with the vendor.

CAUTIONS

• **Ease of use: **H2O.ai's toolchain is primarily code-centric. Although this typically increases flexibility and scalability, it impedes ease of use and reuse.

• **Data preparation and interactive visualization: **These capabilities are problematic for all code-centric platforms, of which H2O.ai's is one. Nonetheless, H2O.ai's platform will prove challenging for clients expecting more interactivity and better, easier-to-use data ingestion, preparation and visualization capabilities. Capabilities for the entire early part of the data pipeline are far less developed than the quantitative parts of H2O.ai's offerings.

• **Business model: **H2O.ai is a full-stack open-source provider — even its most advanced products are free to download (except for the closed-source Driverless AI). H2O.ai derives nearly all its revenue from subscriptions to technical support. Given the development of H2O.ai's revenue lately, we are slightly less concerned than we would otherwise have been about this policy of "giving everything away." However, we still maintain that this policy is difficult to scale. In the long run, H2O.ai will need to consider a more scalable software-licensing model and support infrastructure.