Sparkling Water is about making machine learning simple, speedy, and scalable with Apache Spark. This blog provides an overview of the following new features:  No H2O Client on Spark Driver Speedups Automatic String conversion to Categoricals No H2O Client on Spark Driver Previously, Sparkling Water always started worker nodes either on Spark executors in case […]

H2O is an open-source, in-memory platform for distributed, scalable machine learning. A perfect match for deployment on a Kubernetes cluster, the very modern way of deploying, serving & scaling applications. With the major release 3.30.0.1, released in Q1 2020, H2O obtained first class Kubernetes support. This article explains how to create H2O deployment on Kubernetes. It also covers the […]

We are excited to be named a Visionary in the new Gartner Magic Quadrant for Cloud AI Developer Services (Feb 2020), and have been recognized for both our completeness of vision and ability to execute in the emerging market for cloud-hosted artificial intelligence (AI) services for application developers. This is the second Gartner MQ that […]

2020: A new year, a new decade, and with that, I’m taking a new and deeper look at the technology H2O.ai offers for building AI and machine learning systems. I’ve been interested in H2O.ai since its early days as a company (it was 0xdata back then) in 2014. My involvement had been only peripheral, but […]

We are named a Visionary in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms (Feb 2020).  We have been positioned furthest to the right for completeness of vision among all the vendors evaluated in the quadrant. So let’s walk you through the key strengths of our machine learning platforms. Automatic Machine Learning […]

H2O-3 is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, the machine learning algorithms are implemented. At H2O.ai, we design every operation, be it data transformation, training of machine learning models or even parsing to utilize the distributed computation model. In order to work with big data […]

Super Bowl LIV came and went. The San Francisco 49ers vs the Kansas City Chiefs. Personally, being from the The Bay, I was rooting for the 49ers, but you can’t always get what you want. Whoever came out on top, though, we were all looking forward to a great game full of fantastic plays and […]

Machine-learning models created with H2O may be exported in two basic ways: Binary format, Model Object, Optimized (MOJO). An H2O model can be saved in a binary format, which is tied to the very specific version of H2O it has been created with. There are multiple reasons for such a restriction. One of the important reasons is […]

The demand for machine learning systems has soared over the past few years. This is majorly due to the success of Machine Learning techniques in a wide range of applications. AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to use machine learning models to address complex scenarios. […]

This blog was originally published on towardsdatascience: https://towardsdatascience.com/getting-started-with-h2o-using-flow-b560b5d969b8 A look into H2O’s open-source UI for combining code execution, text, plots, and rich media in a single document. Data collection is easy. Decision making is hard. Today, we have access to a humungous amount of data, which is only increasing day by day. This is primarily […]