<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/danger-caution-h2o-steam-is-very-hot/"></div></div><div><a data-url="https://www.r-bloggers.com/danger-caution-h2o-steam-is-very-hot/" data-counturl="https://www.r-bloggers.com/danger-caution-h2o-steam-is-very-hot/" data-count="horizontal" data-text="Danger, Caution H2O steam is very hot!!" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://longhowlam.wordpress.com/2016/09/30/danger-caution-h2o-steam-is-very-hot/"> R – Longhow Lam's Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img alt="blog_steam" class="wp-image-2014" src="https://i2.wp.com/longhowlam.files.wordpress.com/2016/09/blog_steam.png?resize=456%2C257&ssl=1" data-recalc-dims="1"></p>
<p>H2O has recently released its <a target="_blank" rel="nofollow noopener" href="http://www.h2o.ai/steam/">steam AI engine</a>, a fully open source engine that support the management and deployment of machine learning models. Both H2O on R and H2O steam are easy to set up and use. And both complement each other perfectly.</p>
<h2>A very simple example</h2>
<p>Use H2O on R to create some predictive models. Well, due to lack of inspiration I just used the iris set to create some binary classifiers.</p>
<p><img alt="blogcode" class="wp-image-2018" src="https://i1.wp.com/longhowlam.files.wordpress.com/2016/09/blogcode.jpg?resize=456%2C351&ssl=1" data-recalc-dims="1"></p>
<p>Once these models are trained, they are available for use in the H2O steam engine. A nice web interface allows you to set up a project in H2O steam to manage and display summary information of the models.</p>
<p><img alt="blogsteam2" class="wp-image-2023" src="https://i0.wp.com/longhowlam.files.wordpress.com/2016/09/blogsteam2.jpg?w=456&ssl=1" data-recalc-dims="1"></p>
<p>In H2O steam you can select a model that you want to deploy. It becomes a service with a REST API, a page is created to test the service.</p>
<p><img alt="blogsteam3" class="wp-image-2027" src="https://i0.wp.com/longhowlam.files.wordpress.com/2016/09/blogsteam3.jpg?w=456&ssl=1" data-recalc-dims="1"></p>
<p><strong>And that is it!</strong> Your predictive model is up and running and waiting to be called from any application that can make REST API calls.</p>
<p>There is a lot more to explore in H2O steam, but be careful H2O steam is <strong>very hot!</strong></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://longhowlam.wordpress.com/2016/09/30/danger-caution-h2o-steam-is-very-hot/"> R – Longhow Lam's Blog</a></strong>.</div>
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