H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise

Driverless AI


Series C round led by Wells Fargo and NVIDIA

MOUNTAIN VIEW, CA – November 30, 2017 – H2O.ai, the leading company bringing AI to enterprises, today announced it has completed a $40 million Series C round of funding led by Wells Fargo and NVIDIA with participation from New York Life, Crane Venture Partners, Nexus Venture Partners and Transamerica Ventures, the corporate venture capital fund of Transamerica and Aegon Group. The Series C round brings H2O.ai’s total amount of funding raised to $75 million. The new investment will be used to further democratize advanced machine learning and for global expansion and innovation of Driverless AI, an automated machine learning and pipelining platform that uses “AI to do AI.”

H2O.ai continued its juggernaut growth in 2017 as evidenced by new platforms and partnerships. The company launched Driverless AI, a product that automates AI for non-technical users and introduces visualization and interpretability features that explain the data modeling results in plain English, thus fostering further adoption and trust in artificial intelligence.

H2O.ai has partnered with NVIDIA to democratize machine learning on the NVIDIA GPU compute platform. It has also partnered with IBM, Amazon AWS and Microsoft Azure to bring its best-in-class machine learning platform to other infrastructures and the public cloud.

H2O.ai co-founded the GPU Open Analytics Initiative (GOAI) to create an ecosystem for data developers and researchers to advance data science using GPUs, and has launched H2O4GPU, a collection of the fastest GPU algorithms on the market capable of processing massive amounts of unstructured data up to 40x faster than on traditional CPUs.

“AI is eating both hardware and software,” said Sri Ambati, co-founder and CEO at H2O.ai. “Billions of devices are generating unprecedented amounts of data, which truly calls for distributed machine learning that is ubiquitous and fast. Our focus on automating machine learning makes it easily accessible to large enterprises. Our maker culture fosters deep trust and teamwork with our customers, and our partnerships with vendors across industry verticals bring significant value and growth to our community. It is quite supportive and encouraging to see our partners lead a significant funding round to help H2O.ai deliver on its mission.”

“AI is an incredible force that’s sweeping across the technology landscape,” said Jeff Herbst, vice president of business development at NVIDIA. “H2O.ai is exceptionally well positioned in this field as it pursues its mission to become the world’s leading data science platform for the financial services industry and beyond. Its use of GPU-accelerated AI provides powerful tools for customers, and we look forward to continuing our collaboration with them.”

“It is exhilarating to have backed the H2O.ai journey from day zero: the journey from a PowerPoint to becoming the enterprise AI platform essential for thousands of corporations across the planet,” said Jishnu Bhattarcharjee, managing director at Nexus Venture Partners. “AI has arrived, transforming industries as we know them. Exciting scale ahead for H2O, so fasten your seat belts!”

As the leading open-source platform for machine learning, H2O.ai is leveling the playing field in a space where much of the AI innovation and talent is locked up inside major tech titans and thus inaccessible to other enterprises. This is precisely why over 100,000 data scientists, 12,400 organizations and nearly half of the Fortune 500 have embraced H2O.ai’s suite of products that pack the productivity of an elite data science team into a single solution.

“We are delighted to lead H2O.ai’s funding round. We have been following the company’s progress and have been impressed by its high-caliber management team and success in establishing an open-source machine learning platform with wide adoption across many industries. We are excited to support the next phase of their development,” said Basil Darwish, director of strategic investments at Wells Fargo Securities.

Beyond its open source community, H2O.ai is transforming several industry verticals and building strong customer partnerships. Over the past 18 months, the company has worked with PwC to build PwC’s “GL.ai,” a revolutionary bot that uses AI and machine learning to ‘x-ray’ a business and detect anomalies in the general ledger. The product was named the ‘Audit Innovation of the Year‘ by the International Accounting Bulletin in October 2017.

H2O’s signature community conference, H2O World will take place on December 4-5, 2017 at the Computer History Museum in Mountain View, Calif.

About H2O.ai

H2O.ai’s mission is to democratize machine learning through its leading open source software platform. Its flagship product, H2O.ai empowers enterprise clients to quickly deploy machine learning and predictive analytics to accelerate business transformation for critical applications such as predictive maintenance and operational intelligence. H2O.ai recently launched Driverless AI, the first solution that allows any business — even ones without a team of talented data scientists — to implement AI to solve complex business problems. The product was reviewed and selected as Editor’s Choice in InfoWorld. Customers include Capital One, Progressive Insurance, Comcast, Walgreens and Kaiser Permanente. For more information and to learn more about how H2O.ai is transforming businesses, visit www.h2o.ai.

Contacts

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Laying a Strong Foundation for Data Science Work

By William Merchan, CSO, DataScience.com

In the past few years, data science has become the cornerstone of enterprise companies’ efforts to understand how to deliver better customer experiences. Even so, when DataScience.com commissioned Forrester to survey over 200 data-driven businesses last year, only 22% reported they were leveraging big data well enough to get ahead of their competition.

That’s because there’s a big difference between building predictive models and putting them into production effectively. Data science teams need the support of IT from the very beginning to ensure that issues with large-scale data management, governance, and access don’t stand in the way of operationalizing key insights about your customers. However, many enterprise companies are still treating IT involvement as an afterthought, which ultimately delays the timeline for seeing value from their data science efforts.

There are many ways that better IT management can help scale the impact of data science at your organization. Three best practices include using containers for data science environments, managing compute resources effectively, and putting work into production faster with the help of tools. Here’s how it’s done.

1. Using software containers is one of the most impactful steps you can take to implement IT management best practices. These standardized development environments ensure that the hard work your data scientists put into building predictive models won’t go to waste when it’s time to deploy their code. Without a container-based workflow, a data scientist starting a new analysis must either wait for IT to build an environment from scratch, or build one themselves using the unique combination of packages and resources they prefer — and waiting for those to install or compile.

There are two major issues associated with both of these approaches: they don’t scale, and they’re slow. When data scientists are individually responsible for configuring environments as needed, their work isn’t reproducible — if it’s used in a different environment, it might not even run. Containers put the power in the hands of IT to standardize environment configuration in advance using images, which are snapshots of containers. Data scientists can launch environments from those images — which have already been vetted by IT — saving a lot of time in the long run.

2. Provide ample computing power to support your data scientists’ analysis from start to finish. Empowering them to spin up compute resources in the cloud as needed ensures they never get held up by limited computing power. It also eliminates the potential additional cost of maintaining unnecessary nodes. The same idea applies to on-prem data centers. IT must carefully monitor the expansion of data science work and scale resources accordingly. It may seem obvious, but IHS Markit reports that companies not anticipating this need lose approximately $700 billion a year to IT downtime.

3. Put data science work into production right away to start seeing its value earlier on. Imagine your data science team has built a recommender system to predict what products a customer is likely to enjoy based on the products he or she has already purchased. Even if you’re satisfied with the model’s accuracy and have identified some unexpected relationships that should inform your targeting strategies, this information still needs to be integrated into your application or website for it to be valuable.

Traditionally, the pipeline that delivers those recommendations to your customers would be built by engineers and require extensive support from IT. The rise of microservices, however, gives data scientists the opportunity to deploy models as APIs that can be integrated directly into an application.

If you’re among the 78% of companies not fully realizing the return on your data science investment, chances are there’s room to improve the IT foundation you’ve laid. To learn more about the next steps, find out how to take an agile approach to data science.

About the Author

William Merchan leads business and corporate development, partner initiatives, and strategy at DataScience.com as chief strategy officer. He most recently served as SVP of Strategic Alliances and GM of Dynamic Pricing at MarketShare, where he oversaw global business development and partner relationships, and successfully led the company to a $450 million acquisition by Neustar.