Apps Run The World Apps Purchases

Customer	Industry	Empl.	Revenue	Country	Vendor	Application	Category	When	VAR/SI	Insight Source
G5	Professional Services	250	$50M	United States	H2O.ai	H2O Driverless AI	ML and Data Science Platforms	2016	n/a	In 2016, G5 implemented H2O Driverless AI in partnership with H2O.ai to power its Intelligent Marketing Cloud, embedding ML and Data Science Platforms capabilities into core marketing workflows. G5 implemented H2O Driverless AI as an ML and Data Science Platforms solution to support marketing and lead prioritization functions across the property management marketplace. The implementation centered on automated machine learning capabilities native to H2O Driverless AI, including automated feature engineering, model selection and validation, model interpretability and production scoring. The G5 data science team configured end to end modeling pipelines inside H2O Driverless AI, generating lead propensity scores and model explainability artifacts for use by downstream campaign systems. H2O Driverless AI was integrated into the Intelligent Marketing Cloud to operationalize real time and batch scoring for inbound leads, enabling prioritized lead routing and campaign-level spend allocation. Operational scope included marketing, demand generation and sales use cases within G5 customer deployments in the property management marketplace, with models feeding campaign workflows and lead management systems. Governance and rollout were led by the G5 data science organization, which implemented model validation, monitoring and explainability controls to support decisioning in campaign orchestration. The deployment is positioned to prioritize inbound leads to drive conversions and to reduce digital marketing spend as stated in the implementation notes.
Cisco Systems	Professional Services	90400	$53.8B	United States	H2O.ai	H2O Open Source ML	ML and Data Science Platforms	2014	n/a	In 2014, Cisco Systems partnered with H2O.ai to deploy H2O Open Source ML. The deployment targeted expansion of enterprise modeling capabilities within the ML and Data Science Platforms category, focusing on in-memory distributed processing to accelerate large scale predictive modeling. Cisco implemented H2O Open Source ML to provide core machine learning modules including logistic regression, linear regression, generalized linear models, Naive Bayes, Random Forest, Ada Boosting, and deep learning. The platform was used for ensemble workflows that combine hundreds of simple models into high performing predictors, leveraging H2O specific in-memory compression and distributed algorithms for throughput and scalability. The technical architecture emphasized in-memory distributed execution and compression to reduce footprint, making it possible to handle billions of data rows with a relatively small cluster. This approach was positioned as materially faster than disk based MapReduce implementations, with H2O documentation noting distributed in-memory algorithms are usually 100 times faster and that compression frequently yields 50 to 100 times reductions in in memory data size, enabling some teams to operate on a few machines rather than very large clusters. Governance and rollout focused on enabling data science and analytics teams across departments that lacked large cluster operations, shifting modeling processes toward ensemble and deep learning pipelines within the H2O Open Source ML environment. The vendor selection followed an evaluation of several big data machine learning platforms and emphasized tradeoffs in speed, memory efficiency, and operational resource requirements as primary decision criteria.
	Professional Services	6500	$2.1B	Netherlands	H2O.ai	H2O Open Source ML	ML and Data Science Platforms	2016	n/a
	Professional Services	150	$12M	United States	H2O.ai	H2O Open Source ML	ML and Data Science Platforms	2016	n/a
	Professional Services	15000	$5.7B	United States	H2O.ai	H2O Open Source ML	ML and Data Science Platforms	2016	n/a
	Professional Services	65200	$20.6B	United States	H2O.ai	H2O Open Source ML	ML and Data Science Platforms	2016	n/a
	Professional Services	3500	$500M	United States	H2O.ai	H2O Driverless AI	ML and Data Science Platforms	2017	n/a
	Professional Services	1279	$195M	Germany	Altair Engineering	Altair AI Cloud (formerly RapidMiner Cloud)	ML and Data Science Platforms	2016	n/a
	Professional Services	600	$102M	Netherlands	Domino Data Lab	Domino Data Science Platform	ML and Data Science Platforms	2016	n/a
	Professional Services	160	$20M	United States	DataRobot	DataRobot Cloud	ML and Data Science Platforms	2016	n/a

Logo

Customer

Industry

Empl.

Revenue

Country

Vendor

Application

Category

When

VAR/SI

Insight

Insight Source

Professional Services

250

$50M

United States

H2O.ai

H2O Driverless AI

ML and Data Science Platforms

2016

n/a

In 2016, G5 implemented H2O Driverless AI in partnership with H2O.ai to power its Intelligent Marketing Cloud, embedding ML and Data Science Platforms capabilities into core marketing workflows. G5 implemented H2O Driverless AI as an ML and Data Science Platforms solution to support marketing and lead prioritization functions across the property management marketplace. The implementation centered on automated machine learning capabilities native to H2O Driverless AI, including automated feature engineering, model selection and validation, model interpretability and production scoring. The G5 data science team configured end to end modeling pipelines inside H2O Driverless AI, generating lead propensity scores and model explainability artifacts for use by downstream campaign systems. H2O Driverless AI was integrated into the Intelligent Marketing Cloud to operationalize real time and batch scoring for inbound leads, enabling prioritized lead routing and campaign-level spend allocation. Operational scope included marketing, demand generation and sales use cases within G5 customer deployments in the property management marketplace, with models feeding campaign workflows and lead management systems. Governance and rollout were led by the G5 data science organization, which implemented model validation, monitoring and explainability controls to support decisioning in campaign orchestration. The deployment is positioned to prioritize inbound leads to drive conversions and to reduce digital marketing spend as stated in the implementation notes.

Cisco Systems

Professional Services

90400

$53.8B

United States

H2O.ai

H2O Open Source ML

ML and Data Science Platforms

2014

n/a

In 2014, Cisco Systems partnered with H2O.ai to deploy H2O Open Source ML. The deployment targeted expansion of enterprise modeling capabilities within the ML and Data Science Platforms category, focusing on in-memory distributed processing to accelerate large scale predictive modeling. Cisco implemented H2O Open Source ML to provide core machine learning modules including logistic regression, linear regression, generalized linear models, Naive Bayes, Random Forest, Ada Boosting, and deep learning. The platform was used for ensemble workflows that combine hundreds of simple models into high performing predictors, leveraging H2O specific in-memory compression and distributed algorithms for throughput and scalability. The technical architecture emphasized in-memory distributed execution and compression to reduce footprint, making it possible to handle billions of data rows with a relatively small cluster. This approach was positioned as materially faster than disk based MapReduce implementations, with H2O documentation noting distributed in-memory algorithms are usually 100 times faster and that compression frequently yields 50 to 100 times reductions in in memory data size, enabling some teams to operate on a few machines rather than very large clusters. Governance and rollout focused on enabling data science and analytics teams across departments that lacked large cluster operations, shifting modeling processes toward ensemble and deep learning pipelines within the H2O Open Source ML environment. The vendor selection followed an evaluation of several big data machine learning platforms and emphasized tradeoffs in speed, memory efficiency, and operational resource requirements as primary decision criteria.

Professional Services

6500

$2.1B

Netherlands

H2O.ai

H2O Open Source ML

ML and Data Science Platforms

2016

n/a

Professional Services

150

$12M

United States

H2O.ai

H2O Open Source ML

ML and Data Science Platforms

2016

n/a

Professional Services

15000

$5.7B

United States

H2O.ai

H2O Open Source ML

ML and Data Science Platforms

2016

n/a

Professional Services

65200

$20.6B

United States

H2O.ai

H2O Open Source ML

ML and Data Science Platforms

2016

n/a

Professional Services

3500

$500M

United States

H2O.ai

H2O Driverless AI

ML and Data Science Platforms

2017

n/a

Professional Services

1279

$195M

Germany

Altair Engineering

Altair AI Cloud (formerly RapidMiner Cloud)

ML and Data Science Platforms

2016

n/a

Professional Services

600

$102M

Netherlands

Domino Data Lab

Domino Data Science Platform

ML and Data Science Platforms

2016

n/a

Professional Services

160

$20M

United States

DataRobot

DataRobot Cloud

ML and Data Science Platforms

2016

n/a

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