Ask any data scientist in your workplace. Any Data Science Supervised Learning ML/AI project will go through many steps and iterations before it can be put in production. Starting with the question of “Are we solving for a regression or classification problem?” Data Collection & Curation Are there Outliers? What is the Distribution? What do […]

This blog post demonstrates how H2O’s powerful automatic machine learning can be used together with the Spark in Sparkling Water. We show the benefits of Spark & H2O integration, use Spark for data munging tasks and H2O for the modelling phase, where all these steps are wrapped inside a Spark Pipeline. The integration between Spark […]

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, […]

Prepared by: Erin LeDell, Navdeep Gill & Ray Peck 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 that can be used by non-experts and experts, […]

H2O.ai, Continuum Analytics, and MapD Technologies have announced the formation of the GPU Open Analytics Initiative (GOAI) to create common data frameworks enabling developers and statistical researchers to accelerate data science on GPUs. GOAI will foster the development of a data science ecosystem on GPUs by allowing resident applications to interchange data seamlessly and efficiently. […]

This is a repost from this article on MSDN. We’re hosting an upcoming webinar to present you how to use H2O on HDInsight and to answer your questions. Sign up for our upcoming webinar on combining H2O and Azure HDInsight. We recently announced that H2O and Microsoft Azure HDInsight have integrated to provide Data Scientists […]

This is a guest post from our friends at Kensu. In the space of Data Science development in enterprises, two outstanding scalable technologies are Spark and H2O. Spark is a generic distributed computing framework and H2O is a very performant scalable platform for AI. Their complementarity is best exploited with the use of Sparkling Water. […]

Prepared by: Erin LeDell and Navdeep Gill Stacked Ensembles H2O’s new Stacked Ensemble method is a supervised ensemble machine learning algorithm that finds the optimal combination of a collection of prediction algorithms using a process called stacking or “Super Learning.” This method currently supports regression and binary classification, and multiclass support is planned for a […]

We were very excited to meet with our advisors (Prof. Stephen Boyd, Prof. Rob Tibshirani and Prof. Trevor Hastie) at H2O.AI on Jan 6, 2017. Professors Boyd, Tibshirani & Hastie in the house! @h2oai #elementsofstatisticallearning #MachineLearning pic.twitter.com/FnlCNrY7Hy — H2O.ai (@h2oai) January 6, 2017 Our CEO, Sri Ambati, made two great observations at the start of the meeting: […]

At H2O, we have recently debuted a new feature called ISax that works on time series data in an H2O Dataframe. ISax stands for Indexable Symbolic Aggregate ApproXimation, which means it can represent complex time series patterns using a symbolic notation and thereby reducing the dimensionality of your data. From there you can run H2O’s […]