Mountain View, California-based H2O.ai, which provides a cloud platform for AI system development, today announced that it raised $100 million in a series E round led by Commonwealth Bank of Australia (CBA) with participation from Goldman Sachs, Pivot Investment Partners, Crane Venture Partners, Celesta Capital, and others. It brings H2O’s total raised to over $251 million and values the company at $1.6 billion, as H2O partners with CBA to boost the latter’s AI capabilities and generate” better customer and community outcomes at a more rapid pace and … scale.”

H2O (originally Oxdata) was founded in 2012 by Sri Satish Ambati, who previously served as a research assistant at the Indian Space Research Organization. It sprung up from an open source project designed to integrate with data science workflows written in the programming language R. Forced to lay off a portion of its workforce early on, H2O pivoted in 2016 to working closely with a few major customers and launching cloud services that could be promoted through conventional sales and marketing channels.

H2O’s products today are designed to simplify machine learning deployment across verticals like financial services, insurance, health care, telecommunications, retail, pharmaceutical, and marketing. The company offers prebuilt models and apps for use cases like customer churn prediction, sales targeting, spend optimization, credit risk scoring, anti-money laundering, predictive maintenance, customer monitoring, malicious domain detection, and more.

“Our AI cloud platform delivers a deep set of capabilities that not only help data scientists evaluate their models … but also help explain [the] models to business users and executives. In addition, the integrated platform hosts and monitors the ethically built and understood models, to ensure ongoing business success,” Ambati told VentureBeat via email. “We work closely with our customers to ensure the success of their AI initiatives and are able to help our customers move from ‘lab experiments’ to real business value.”


H2O’s flagship product, which can be used to create a range of statistical models and algorithms, runs on top of existing datacenter clusters. Its AutoML functionality automatically runs through models and their parameters to produce a leaderboard of the best models, tapping technologies like distributed systems and in-memory computing to accelerate data processing.

H2O guides customers through the process of creating their own AI-powered apps and services. They can create recipes that extend the platform as well as add administration and collaboration features for model management and implementation, such as health checks and data science metrics around drift detection, model degradation, A/B testing, and alerts for recalibration and retraining.

Above: H2O’s model development and monitoring dashboard.

For customers with specific requirements, H2O offers enterprise support with training, account managers, and prioritized issue resolution. Subscription plans include access to tools for orchestrating machine learning models across larger datacenter clusters.

H2O recently partnered with AT&T to build and launch an AI feature store that manages and reuses data, housing and distributing the features needed to build AI models. (In machine learning, features — variables that act like input data– are used by models to make predictions.) H2O also recently announced platform integrations with data analytics tool provider Teradata and AI platform KNIME to enable “workflow management across the entire data science lifecycle,” in H2O’s words.

Growth in AI adoption

As companies face pandemic headwinds including worker shortages and supply chain disruptions, they’re increasingly turning to AI for efficiency gains. According to a recent Algorithmia survey, 50% of enterprises plan to spend more on AI and machine learning in 2021, with 20% saying they will be “significantly” increasing their budgets. In a 2020 report, analysts at McKinsey wrote, “[S]ome companies are capturing value from AI at the enterprise level, and many are generating revenue and cost reductions at least at the function level.”

But top challenges around AI remain, particularly when it comes to ingesting, processing, and managing training data. Data scientists spend the bulk of their time cleaning and organizing data. And respondents to Alation’s latest quarterly State of Data Culture Report said that inherent biases in the data being used in their AI systems produce discriminatory results that create compliance risks for their organizations.

That’s one of the reasons that managed and automated AI development platforms like H2O have gained ground in recent years. (The global AutoML market along generated $270 million in revenue in 2019.) H2O’s competitors include Amazon SageMaker, Azure Cognitive Services, and Google’s Cloud AutoML, as well as startups like DataRobot and Abacus.ai. But H2O has managed to nab over 20,000 organizations as customers to date, including over half of the Fortune 500.

For example, jewelry insurer Jewelers Mutual is using H2O’s platform to build models that can identify which customers need additional physical security personnel to protect their inventory during California wildfires. Nationwide, another customer, has tapped H2O’s tools to create systems for customer retention, call routing, and fraud prevention. And İşbank, a private bank in Turkey, has developed models for income prediction, cash forecasting, and check default prediction leveraging H2O’s solutions.

CBA CEO Matt Comyn said that the investment in 300-employee H2O will boost the bank’s ability to offer AI-powered products and services to customers. Specifically, he expects it’ll bolster the bank’s analytics capabilities and help enhance the predictive accuracy of its existing models, so that CBA can offer “more personalized and targeted solutions” at a faster rate.

“One of the strengths of CBA, with its large market share and broad coverage across all aspects of the Australian economy, is the large amount of data that it securely holds and the technological infrastructure it has to capture data,” Comyn said in a press release. “Customers trust us to use that for good, and this partnership will help us accelerate that.”


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