January 26th, 2021

Introducing H2O AI Hybrid Cloud

RSS icon RSS Category: Cloud, H2O AI Hybrid Cloud, Kubernetes

Organizations have made large investments in modernizing their data infrastructure and operations, but most still struggle to drive maximum value from their data.  Many companies experimented with building large teams of expert data scientists, and while this approach did produce some valuable models, the cost was high and the timeframes long.  In addition, many of the models’ data science teams invested in, never made into the hands of business users, so the value was never realized.  Organizations needed a way to rapidly make models, make AI apps that use the models, and share AI apps with business users across the organization. 

Today we’re announcing H2O AI Hybrid Cloud, an end-to-end platform that enables organizations to rapidly make world-class AI models and applications for virtually any use case.  

H2O AI Hybrid Cloud brings automation capabilities across the entire data science lifecycle, including connecting to and preparing data, building and explaining models, and deploying and operating them.  Additionally, the platform also makes it easy to build and share AI applications across an entire organization. H2O AI Hybrid Cloud is optimal for organizations that want to drive value from their data by putting AI into the hands of their business users.

The platform runs on Kubernetes, allowing customers to run on any cloud or on-premise infrastructure and operate in an elastic manner.  

H2O AI Hybrid Cloud includes: 

  • H2O3 – High scale open source machine learning
  • H2O Driverless AI – Award-winning AutoML
  • H2O MLOps – Governance and continuous model improvement
  • H2O AI AppStore – Publish and share AI Apps


Building and operating AI applications requires a lot more planning than merely training a machine learning model. Issues around deployment, maintenance, scalability, and the version control of different libraries must be considered. Fortunately, we also have relevant technologies such as Kubernetes to solve such problems.

Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and managing of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. 

Having Kubernetes integrated with our H2O AI Hybrid Cloud, our users can create, deploy, scale, and most importantly, manage multiple cloud-native AI applications with ease.

H2O Wave – Make Really Real Time AI Apps

H2O Wave is an open-source Python development framework that makes it fast and easy for data scientists, machine learning engineers, and software developers to develop interactive AI apps with sophisticated visualizations. H2O Wave integrates with H2O AI Hybrid Cloud and accelerates development with a wide variety of user-interface components and charts, including dashboard templates, dialogs, themes, widgets, and many more.

H2O Wave’s low latency design enables truly real-time streaming for all your applications.

H2O AI AppStore

The goal of virtually all Machine Learning projects at large companies is to develop successful AI applications that are heavily used by the business.

The H2O AI AppStore enables a single location to seamlessly view and share the machine learning applications being built across your company. For example: 

  • Credit Risk
  • Customer Churn
  • Sales Forecasting
  • Social Media Sentiment
  • Explainable Hotel Ratings
  • Online Shopping Recommendations

Additionally, the AppStore hosts many core data science and preparation methods such as data connectors, clustering, and NLP/Data Labeling to enable fast, modular development of customized data science workflows.

Provisioning & Governance

By creating a unified AppStore within the H2O AI Hybrid Cloud, the platform enables advanced provisioning and governance of all the data science workstreams and projects in progress. 

The seamless integration between H2O Driverless AI and H2O MLOps allows our users to deploy models to different environments (e.g., development or production) with just a few clicks. 

Model monitoring is another crucial step in the modern machine learning life cycle. With H2O MLOps, we can easily configure an active monitoring system and get notified when specific drifts are detected.

Any Tech, Any Task

Not only can you leverage the entire ecosystem of H2O products (H2O3, Driverless AI, MLOps, AutoDoc, MOJOs), you can also seamlessly integrate any Python libraries to extend your analysis at any step of the data science pipeline: from EDA to Model Management. Check out recipes in H2O Driverless AI for more information.

How to Get Started

Ready to try it? If you are looking to learn more about how the H2O AI Hybrid Cloud can fit into your digital and AI transformation, reach out to us here, and our team can help you start making. 

The Wave SDK is open source, so just download it from our GitHub website and follow the instructions for Windows/Mac/Linux. You will also find the links to examples and API there. Enjoy!

About the Authors

Benjamin Cox

Ben Cox is a Director of Product Marketing at H2O.ai where he helps lead Responsible AI market research and thought leadership. Prior to H2O.ai, Ben held data science roles in high-profile teams at Ernst & Young, Nike, and NTT Data. Ben holds a MBA from the University of Chicago Booth School of Business with multiple analytics concentrations and a BS in Economics from the College of Charleston.

Jo-Fai Chow

Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O's EMEA meetup groups including London Artificial Intelligence & Deep Learning - one of the biggest data science communities in the world with more than 11,000 members.

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