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It’s all Water (or should I say H2O) to me!
By Krishna Visvanathan, Co-founder & Partner, Crane Venture Partners In the career of any venture capitalist, one dreads the “oh shit moment” . For those unfamiliar with this most technical of terms – it is that moment of clarity when a VC, in the immediate aftermath of closing one’s latest investment (often at the first post invest...
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H2O4GPU Hands-On Lab (Video) + Updates
Aggregator DBSCAN Kalman Filters K-nearest neighbors Quantiles Sort If you’d like to learn more about H2O4GPU, I invite you to explore these helpful links: H2O4GPU README Open Source License (Apache 2.0) Happy Holidays! Rosalie ...
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Driverless AI - Introduction, Hands-On Lab and Updates
#H2OWorld was an incredible experience. Thank you to everyone who joined us! There were so many fascinating conversations and interesting presentations. I’d love to invite you to enjoy the presentations by visiting our YouTube channel . Over the next few weeks, we’ll be highlighting many of the talks. Today I’m excited to share two prese...
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New versions of H2O-3 and Sparkling Water available
Dear H2O Community, #H2OWorld is on Monday and we can’t wait to see you there! We’ll also be live streaming the event starting at 9:25am PST. Explore the agenda here . Today we’re excited to share that new versions of H2O-3 and Sparkling Water are available. We invite you to download them here: http://www.h2o.ai/download/ H2O-3.16 – MO...
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H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise
November 30, 2017 | Data Science, Machine Learning | H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise
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Laying a Strong Foundation for Data Science Work
By William Merchan, CSO, DataScience.com In the past few years, data science has become the cornerstone of enterprise companies’ efforts to understand how to deliver better customer experiences. Even so, when DataScience.com commissioned Forrester to survey over 200 data-driven businesses last year, only 22% reported they were leverag...
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H2O.ai Releases H2O4GPU, the Fastest Collection of GPU Algorithms on the Market, to Expedite Machine Learning in Python
H2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn Python API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algor...
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Driverless AI Blog
In today’s market, there aren’t enough data scientists to satisfy the growing demand for people in the field. With many companies moving towards automating processes across their businesses (everything from HR to Marketing), companies are forced to compete for the best data science talent to meet their needs. A report by McKinsey says th...
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Scalable Automatic Machine Learning: Introducing H2O's AutoML
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...
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XGBoost in the H2O Machine Learning Platform
The new H2O release 3.10.5.1 brings a shiny new feature – integration of the powerful XGBoost library algorithm into H2O Machine Learning Platform! XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. XGBoost provides parallel tree boosting (also known as GBDT, GBM) that ...
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H2O Platform Extensibility
The latest H2O release, 3.10.5.1, introduced several new concepts to improve extensibility and modularity of the H2O machine learning platform . This blog post will clarify motivation, explain design decisions we made, and demonstrate the overall approach for this release.MotivationThe H2O Machine Learning platform was designed as a mono...
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Machine Learning on GPUs
With H2O GPU Edition, H2O.ai seeks to build the fastest artificial intelligence (AI) platform on GPUs. While deep learning has recently taken advantage of the tremendous performance boost provided by GPUs, many machine learning algorithms can benefit from the efficient fine-grained parallelism and high throughput of GPUs. Importantly, G...
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The Race for Intelligence: How AI is Eating Hardware - Towards an AI-defined hardware world
With the AI arms race reaching a fever pitch, every data-driven company is (or at least should be) evaluating its approach to AI as a means to make their owned datasets as powerful as they can possibly be. In fact, any business that’s not currently thinking about how AI can transform its operations risks falling behind its competitors and...
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H2O announces GPU Open Analytics Initiative with MapD & Continuum
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 applicat...
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Use H2O.ai on Azure HDInsight
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 with a Lead...
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Sparkling Water on the Spark-Notebook
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 Wat...
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Stacked Ensembles and Word2Vec now available in H2O!
Prepared by: Erin LeDell and Navdeep Gill MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]} }); Stacked Ensembles ensemble <- h2o.stackedEnsemble(x = x, y = y, training_frame = train, base_models = my_models) Python:ensemble = H2OStackedEnsembleEstimator(base_models=my_models) ensemble.train(x=x, y=y, training...
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Artificial Intelligence Is Already Deep Inside Your Wallet – Here’s How
Artificial intelligence (AI) is the key for financial service companies and banks to stay ahead of the ever-shifting digital landscape, especially given competition from Google , Apple , Facebook , Amazon and others moving strategically into fintech. AI startups are building data products that not only automate the ingestion of vast amou...
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Football Flowers
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Start Off 2017 with Our Stanford Advisors
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, ma...
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What is new in Sparkling Water 2.0.3 Release?
This release has H2O core – 3.10.1.2Important Feature:This architectural change allows to connect to existing h2o cluster from sparkling water. This has a benefit that we are no longer affected by Spark killing it’s executors thus we should have more stable solution in environment with lots of h2o/spark node. We are working on article on ...
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