Introducing H2O Community & Support Portals

At H2O, we enjoy serving our customers and the community, and we take pride in making them successful while using H2O products. Today, we are very excited to announce two great platforms for our customers and for the community to better communicate with H2O. Let’s start with our community first:

Community Badge

The success of every open source project depends on a vibrant community, and having an active community helps to convert an average product into a successful product. So to maintain our commitment to our H2O community, we are releasing an updated community platform at This community platform is available for everyone, whether you are new to machine intelligence or are a seasoned veteran. If you are new to machine intelligence or H2O, you have an opportunity to learn from great minds, and if you are a seasoned industry veteran, you can not only enhance your skillset, you can also help others to achieve success.

Our objective is to develop this community in a way where every community member has the opportunity to establish himself or herself as a technology leader or expert by helping others. Every moment you spend here in the community, either by creating or consuming content, will not only help you to learn more, but will also help to establish your own brand as a reputed member of our machine intelligence community. Here are some highlights for our community:

  • The community content is distributed into 3 main sections as below:
    • Questions
    • Ideas
    • Knowledge Base Articles
  • The contents in the above sections is distributed among various technology groups called spaces, i.e. Algorithms, H2O, Sparkling Water, Exception, Debugging, Build, etc.

  • Every content needs to be part of a specific space so that experts in their space can provide faster and better responses. A list of all spaces is here.
  • As a visitor, you are welcome to visit every section of the community and learn from posts from community members.
  • Once logged in as community member using OpenID®, you can ask questions, write knowledge base articles, and propose ideas or feature requests for our products.
  • You are welcome to provide feedback to others’ content by liking the KB, question, or answer or simply by up-voting an idea.
  • As you spend more and more time here in community, you will be given higher roles toward management and improvement of your own community.
  • As logged-in member of community, every activity adds points toward your reputation, and as you spend more time in community, you will rank higher among your peers and establish yourself as an expert or a technology leader.
  • Please make sure you read the Guidelines before posting a question.
  • We are working towards making the site more integrated with other social platforms such as Twitter® and Facebook®, as well as adding support to other OpenID providers.

Now let me introduce our updated enterprise support portal:

Support Badge

H2O has been by over 60K data scientists since its initial release, and now more than 7,000 organizations worldwide use H2O applications, including H2O-3 and Sparkling Water. To assist our enterprise customers, we have revamped our enterprise support portal, which is available at With this new portal, we are able to provide SLA-based, 24×7 support for our enterprise customers. Please visit this page to learn about the H2O enterprise support offering. While this support portal is specially catered to assist our enterprise customers, it is also open for everyone who is using any of the free, open source H2O applications.

You can open a support incident with the H2O support team in one of two ways:

  • Through the Support Portal
    • Please visit to support portal at, and select “NEW SUPPORT INCIDENT”.
    • You don’t need to be logged in to the support portal to open a new incident; however it is advisable to have an account so that you can monitor the ticket progress.
    • You will have an opportunity to set up incident priority, i.e. Low, High, Medium, or Urgent.
  • By Email
    • Send an email to describing your problem clearly.
    • Please attach any other info within the email in zipped format that could be helpful to identify the root cause.

When opening a support incident, please provide your H2O version, your Hadoop or Spark version (if applicable) and any logs, stack dump, or other information that might be helpful when troubleshooting this problem. Whether you are an H2O enterprise customer or just using one of our free, open source H2O applications, both of these venues are open for you to bring your question or comments. We are listening and are here to help.

We look forward to working with you through our community and support portals.

Avkash Chauhan

H2O Support: Customer focused and Community Driven

Interview with Carolyn Phillips, Sr. Data Scientist, Neurensic

During Open Tour Chicago we conducted a series of interviews with data scientists attending the conference. This is the second of a multipart series recapping our conversations.

Be sure to keep an eye out for updates by checking our website or following us on Twitter @h2oai.

AAEAAQAAAAAAAAeRAAAAJGZmMWZiMGE1LTVlMDgtNGQwZi05NzYyLTEwMTMxNDhmODcwMw How did you become a data scientist?

Phillips: Until very close to two months ago I was a computational scientist working at Argonne National Laboratory. Okay.

Phillips: I was working in material science, physics, mathematics, etc., but I was getting bored with that and looking for new opportunities, and I got hired by a startup in Chicago. Yes.

Phillips: When they hired me they said, “We’re hiring you, and your title is Senior Quantitative Analyst,” but the very first day I showed up, they said, “Scratch that. We’ve changed your title. Your title is now Senior Data Scientist.” And I said, “Yes, all right.” It has senior in it, so I’m okay going with that. Nice. I like it.

Phillips: So I’m a mathematician, physicist and computer scientist by training who likes to solve problems with data and algorithms, and so now I’m a data scientist. That’s impressive. I don’t know if people have really wrapped their head around what it means to be a data scientist.

Phillips: I will say that one of the reasons why I started looking around for data scientist positions is that I come from an academic research background. I have a PhD in physics and computing, and a lot of my peers who have a very, very similar background to me – we did research together, we wrote papers together – became frustrated with academic research for various reasons. Many of them said, “Well, rats. I have a skill set that’s valuable,” and they’ve become data scientists. They work at places like Airbnb, they work at consulting firms, they work at startups. Each one of us has reached that point where we’ve said, “I’m frustrated with being an academic researcher.” I saw the direction that many of my peers had gone in saying, “I have a good skill set and it is valuable, and the place right now where that is being valued is in this area called data science, and I shall go into it,” and I said, “That’s a good idea. I’ll do that too.” There you go. That’s my story. Wow, that’s really cool, yeah. I mean, I’m finding that the more people I talk to the greater number of paths towards becoming a data scientist I find. So what’s your biggest pain point as a data scientist?

Phillips: Data preparation. We want to get more data from our companies, and theoretically all this data is being generated by the same software everywhere. But different companies configure that software differently, and it’s a lot of work to make sure all the data you get is formatted in the same way. Yes, I see.

Phillips: Everything I do has to have meaning. For example, I built this beautiful algorithm and I love it, and I applied it to the data, and we found this result in the data, and we said, “What is that? Look at that. Oh, my goodness, what is that? What is that? That’s crazy. That’s terrible, you know, we have to get right on that.” Yes.

Phillips: And I thought, well, before we get too excited, let me dig down to the original raw data that generated this. Dig, dig, dig, dig, dig. Oh, we assumed that data would always come in this format, and this data came in that format, and at the end of the day it looked like something it wasn’t, so I feel like that’s actually the big challenge. Oh, very interesting. Do you have methods of making your data more uniform?

Phillips: Well, I’m not responsible for that directly, but no. Every time we get in a new source of data it’s going to be this painful process of normalizing it so that it looks as much as possible like the other sources of data. Thank you so much, Carolyn. That was really helpful information. It was a pleasure meeting you.

Phillips: You too.

Interview with Svetlana Kharlamova, ­Sr. Data Scientist, Grainger

During Open Tour Chicago we conducted a series of interviews with data scientists attending the conference. This is the first of a multipart series recapping our conversations.

Be sure to keep an eye out for updates by checking our website or following us on Twitter @h2oai.

Svetlana Kharlamova How did you become a data scientist?

Kharlamova: I’m a physicist. Okay.

Kharlamova: I came here from the academia of physics. I worked for seven years in academia for physics and math, and four years ago I switched to finance to be more of a math person than a physics person. I see.

Kharlamova: And from finance I came to the data industry. At that time data science was booming. Oh, okay.

Kharlamova: And I got excited with all new the stuff and technologies coming up, and here I am. Okay, nice. So what business do you work for now?

Kharlamova: I work for Grainger. We’re focused on equipment distribution; serving as a connector between manufacturing plants, factories and consumers. So what are some of the problems that you guys are looking to solve?

Kharlamova: Building recommendation engines for customers. For that you need to leverage natural language processing and positive logic. What resources do you use to stay on top of the information in the data science world? Are there blogs that you read or like, or places that you go?

Kharlamova: Staff communities and data science communities are important sources of information. Yes. That’s great. And is there any advice that you would have for someone who’s an up and coming data scientist, or someone who’s just generally interested in the field?

Kharlamova: Advice to somebody who’s generally interested in the field? Yes, about becoming a data scientist.

Kharlamova: It’s a difficult question, because if a person takes a one year course on Coursera or somewhere else on data science, it doesn’t mean that they’re a data scientist yet, because you need to see the problem in the big picture. Yes.

Kharlamova: You need to be able to identify the challenges, the problem and various solutions. You cannot explore everything. You need to narrow down your choice. Yes, okay.

Kharlamova: You also need to have substantial knowledge of mathematics, statistics and computer science. But understand that you don’t need to immediately start using a sophisticated random forest model. Maybe you can just use simple algebra. Maybe it’s a question of two plus two. Right.

Kharlamova: And then you don’t need all these assumptions and approximations. Because I’m a physicist, I like a defined correct answer much more than something fuzzy. To be successful as a data scientist you need to decide how best to approach a problem then find a solution that’s as simple as possible. Okay. I see. That’s great advice. So it’s not just about having the knowledge, but it’s also about having an approach that is, like you said, simple, that you can probably use more often to provide a clear answer. That’s great, great advice.

H2O Day at Capital One

Here at one of our most important partners is Capital One, and we’re proud to have been working with them for over a year. One of the world’s leading financial services providers, Capital One has a strong reputation for being an extremely data and technology-focused organization. That’s why when the Capital One team invited us to their offices in McLean, Virginia for for a full day of H2O talks and demos we were delighted to accept. Many key members of Capital One’s technology team were among the 500+ attendees at the event, including Jeff Chapman, MVP of Shared Technology, Hiren Hiranandani, Lead Software Engineer, Mike Fulkerson, VP of Software Engineering and Adam Wenchel, VP of Data Engineering.

A major theme throughout the day was “vertical is the new horizontal,” an idea presented by our CEO Sri Ambati, about how every company is becoming a technology company. Sri pointed out that software is becoming increasingly ubiquitous at organizations at the same time that code is becoming a commodity. Today, the only assets that companies can defend is their community and brand. Airbnb is more valuable than most hospitality companies, despite owning no property, and Uber is more valuable than most transportation companies, despite owning no vehicles. And if “software is eating the world” then artificial intelligence (AI) is eating software, as traditional rules-based models no longer cut it in today’s rapidly changing world.

Our partnership started about a year ago, where we met in California, and learned about the value proposition of H2O. To be honest, I think we were all floored by what we saw. – Jeff Chapman

This was obviously an important message for attendees at Capital One, who were looking to learn more about AI and machine learning. Of particular interest was how machine learning and AI can help with use cases such as personalization and fraud detection and how the technology can drive future data-driven decision making. Attendees also had a chance to share their experiences using H2O to analyze and score models with their colleagues across business units. The event fit perfectly into’s vision of a grassroots community that encourages cooperation and the sharing of information. We look forward to continuing to work with Capital One, and all of our partners, to promote the democratization of data science and the growth of open source communities.

Visit us online to find a local event where you can meet with the makers of H2O in-person. Please also don’t forget to see the video of our time at Capital One here!

The Top 10 Most Watched Videos From H2O World 2015

Now that we’re a few months out from H2O World we wanted to share with you all what the most popular talks were by online viewership. The talks covered a variety of topics from introductions, to in-depth examinations of use cases, to wide-ranging panels.

Introduction to Data Science
Featuring Erin LeDell, Statistician and Machine Learning Scientist,
An introductory talk for people new to the field of data science.

Intro to R, Python, Flow
Featuring Amy Wang, Math Hacker,
A hands-on demonstration of how to run H2O in R and Python and an introduction to the Flow GUI.

Machine Learning at Comcast
Featuring Andrew Leamon, Director of Engineering Analysis, Comcast and Chushi Ren, Software Engineer, Comcast
An inside look at how Comcast leverages machine learning across its business units.

Migrating from Proprietary Analytics Stacks to Open Source H2O
Featuring Fonda Ingram, Technical Manager,
A ten-year SAS veteran explains how to migrate from proprietary software to an open source environment.

Top 10 Data Science Pitfalls
Featuring Mark Landry, Product Manager,
A Kaggle champion offers an overview of ten top pitfalls to avoid when performing data science.

Featuring Erin LeDell, Statistician and Machine Learning Scientist,
Another popular talk from Erin, this time providing an overview specifically of ensemble learning.

Sparkling Water
Featuring Michal Malohlava, Software Engineer,
An introduction to Sparkling Water, H2O’s Spark API, by one of its key architects.

Panel – Competitive Data Science
Featuring Arno Candel, Chief Architect,, Phillip Adkins, Data Scientist, Banjo, Nick Kridler, Data Scientist, Stich Fix, Mark Landry, Product Manager,, John Park, Principal Data Scientist, Hewlett-Packard Enterprise, Lauren Savage, Data Scientist, AT&T and Guocong Song, Data Scientist, Playground.Global
A panel discussion covering all aspects of competitive data science.

Survey of Available Machine Learning Frameworks
Featuring Brenden Herger, Data Scientist, Capital One
An overview of available machine learning frameworks and an analysis of why teams use specific ones.

Panel – Industrial Data Science – Practitioners’ Perspective
Featuring SriSatish Ambati, CEO & Cofounder,, Xaviar Amatriain, VP of Engineering, Quora, Scott Marsh, Research & Development Analyst, Progressive Insurance, Taposh Dutta Roy, Manager, Kaiser Permanente, Nachum Shacham, Principal Data Scientist, PayPal and Daqing Zhao, Director of Advanced Analytics, Macy’
A discussion of large data science deployments by the people most familiar with them.

A great selection of talks if we do say so ourselves! Is it too early to start counting the days to H2O World 2016?

Pre-H2O World, Part 1

H2O fans, the team is burning the midnight oil to get H2O World ready for you all. With an audience size twice that of last year’s event we’re going to pack the house at the Computer History Museum! This year’s event will feature 70+ speakers spread out over 41 talks, 22 training sessions and eight panels during the course of the most exciting three days a data scientist could ask for. These folks are amongst the leading lights in our industry including Hilary Mason, Monica Rogati and Stanford Professors Stephen Boyd and Rob Tibshirani.

Right now our awesome new QA team members are burning over 1,000 USB sticks filled to the brim with new content. We’re especially excited for you all to see use cases from your colleagues across a wide variety of industries including ad tech, insurance and finance and from companies like Progressive, Macy’s, PayPal and AT&T. Stay tuned for a follow up outlining all of Monday’s events, we’ve got some surprises in store!

If there’s something YOU want to see at the show Tweet us @H2Oai #h2oworld