This blog originally appeared on Towardsdatascience.com “There were 5 Exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days”: Eric Schmidt If you are an R user, chances are that you have already been using the data.table package. Data.table is an extension of the data.frame package in R. It’s also […]

The Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Jan 2019) is out and H2O.ai has been named a Visionary. The Gartner MQ evaluates platforms that enable expert data scientists, citizen data scientists and application developers to create, deploy and manage their own advanced analytic models. H2O.ai Key Highlights: Some of the highlights […]

There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release 3.22.0.3. Immediately followed by a more major release (but still on 3.22 branch) codename Xu, named after mathematician Jinchao Xu, whose work is focused on deep neural networks, besides many other fields of research. Of course, the […]

Introduction Anomaly detection is a common data science problem where the goal is to identify odd or suspicious observations, events, or items in our data that might be indicative of some issues in our data collection process (such as broken sensors, typos in collected forms, etc.) or unexpected events like security breaches, server failures, and […]

I am very excited to announce the formation of the inaugural community for H2O Driverless AI  users. The Driverless AI Community is open for anyone looking to engage with other users as well as experts from H2O.ai’s Driverless AI,   Driverless AI is an award-winning automatic machine learning platform that does “AI to do AI” to solve […]

Some background: I am a rising senior in highschool, and the summer of 2018, I interned at H2O.ai. With no ML experience beyond Andrew Ng’s Introduction to Machine Learning course on Coursera and a couple of his deep learning courses, I initially found myself slightly overwhelmed by the variety of new algorithms H2O has to […]

In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software (e.g. H2O, scikit-learn, keras). Although these tools have made it easy to train and evaluate machine learning […]

This post originally appeared in the IBM Developer blog here. This post is co-authored by Animesh Singh, Nicholas Png, Tommy Li, and Vinod Iyengar. Deep learning frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Chainer have reduced the effort and skills needed to train and use deep learning models. But for AI developers and data scientists, […]

Today, we are introducing a walkthrough on how to deploy H2O 3 on Kubeflow. Kubeflow is an open source project led by Google that sits on top of the Kubernetes engine. It is designed to alleviate some of the more tedious tasks associated with machine learning. Kubeflow helps orchestrate deployment of apps through the full […]

Congratulations – Thanks to the support of our customer community over the past years, H2O.ai is a leader and one with the most completeness of vision in Gartner Magic Quadrant for Data Science and Machine Learning Platforms. It is an ecosystem we dedicated a good part of this decade to open up and spring. This […]