“H2O is the number one open source machine learning platform for smarter applications. H2O.ai is the silicon valley software company supporting and developing H2O.
Leading insurance, healthcare and financial services companies are using H2O to make smarter predictions about churn, pricing, fraud and more. H2O.ai is fostering a grassroots movement of systems engineers, data scientists, data developers and predictive analysts to move machine learning forward. A rapidly growing community of H2O users is now active in more than 5,000 organizations worldwide.
H2O.ai has seen explosive download growth of 300% YoY, with active installations in over 5,000 organizations.
Over 10 percent of the world’s data scientists now use H2O to create and deploy predictive models. This year alone H2O.ai signed 25 new customers including AT&T, Comcast, Kaiser Permanente, McKesson, Walgreens, Capital One, Progressive, Transamerica Corporation and Zurich Insurance Group.”
“Algorithms are transforming every business,” said Sri Ambati, Co-founder and CEO of H2O.ai. “H2O reduces time to insight without the cost and lock-in of proprietary stacks and operationalizes data science through data products and smart applications.“
H2O.ai – Ep. 14 (Deep Learning SIMPLIFIED)
H2O.ai is a software platform that offers a host of machine learning algorithms, as well as one deep net model. It also provides sophisticated data munging, an intuitive UI, and several built-in enhancements for handling data. However, the tools must be run on your own hardware.
H2O.ai was founded by SriSatish Ambati, Cliff Click, and Arno Candel. In addition to its only deep net – a vanilla MLP – the platform offers a variety of models like GLM, Distributed Random Forest, Naive Bayes, a K-Means clustering model, and a few others. H2O.ai can be linked to multiple data sources in order to train data loads.
The UI is highly intuitive, but you can also work with the tools through other apps like Tableau or Excel. These interfaces allow you to set up a deep net by configuring its hyper-parameters.
H2O.ai needs to be deployed and maintained on your own hardware, which may be a limiting factor. However, the platform comes with many performance enhancements like in-memory map-reduce, columnar compression, and distributed parallel processing. Depending on your hardware’s capabilities, training on big data sets could be completed in a reasonable amount of time. As an added note, it’s unclear whether or not GPU support is a built-in feature at this point in time.
Text-Source: H2O.ai – Media / Youtube
http://www.h2o.ai/