<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>production on DataScience.LA</title><link>https://datasciencela.github.io/tag/production/</link><description>Recent content in production on DataScience.LA</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>DataScience.LA, 2014–2021 · Read-only archive: comments are closed.</copyright><lastBuildDate>Thu, 13 Oct 2022 00:02:42 -0700</lastBuildDate><atom:link href="https://datasciencela.github.io/tag/production/index.xml" rel="self" type="application/rss+xml"/><item><title>XGBoost workshop and meetup talk with Tianqi Chen</title><link>https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/</link><pubDate>Mon, 06 Jun 2016 09:00:50 -0700</pubDate><guid>https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/</guid><description>&lt;p&gt;&lt;a href="https://github.com/dmlc/xgboost"&gt;XGBoost&lt;/a&gt; is a fantastic open source implementation of &lt;a href="https://en.wikipedia.org/wiki/Gradient_boosting"&gt;Gradient Boosting Machines&lt;/a&gt;, a general purpose supervised learning method that achieves the highest accuracy on a wide range of datasets in practical applications. Deep learning is all the hype now, but apart from specific domains such as images, speech or text (i.e. problems with higher-level abstractions to be learnt and/or perception problems, where deep learning achieved some remarkable results indeed), it is usually outperformed by Gradient Boosting in a majority of general business domains and supervised learning applications. Proof of this and also because XGBoost has an easy-to-use interface from both &lt;a href="http://dmlc.ml/rstats/2016/03/10/xgboost.html"&gt;R&lt;/a&gt; and Python, XGBoost has become a favorite tool in Kaggle competitions. Besides feature engineering, cross-validation and ensembling, XGBoost is a key ingredient for achieving the highest accuracy in many data science competitions and more importantly in practical applications.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/xgboost-workshop-and-meetup-talk-with-tianqi-chen/feature.png"/></item><item><title>Secure your Shiny apps (against SQL injection)</title><link>https://datasciencela.github.io/secure-your-shiny-apps-against-sql-injection/</link><pubDate>Mon, 12 Jan 2015 09:25:12 -0800</pubDate><guid>https://datasciencela.github.io/secure-your-shiny-apps-against-sql-injection/</guid><description>&lt;p&gt;&lt;a href="http://shiny.rstudio.com/"&gt;Shiny&lt;/a&gt; takes inputs from UI elements and sends them to the server, where the application can access them as R variables. While Shiny has security measures in place, as in any typical web application, it remains the developer&amp;rsquo;s responsibility to sanitize the inputs before using them. For example, Shiny has no way to protect you if you are using an input in a SQL query such as &lt;code&gt;select ... from ... where field = 'input'&lt;/code&gt;. Someone manipulating the websocket communication can craft a specially-formatted input that can force the database to execute a query that it is not supposed to do, termed an &lt;a href="http://en.wikipedia.org/wiki/SQL_injection"&gt;SQL injection&lt;/a&gt;. This might give an attacker access to private data or the ability to do other nefarious things, and it is a common security issue.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/secure-your-shiny-apps-against-sql-injection/feature.png"/></item><item><title>LA R Meetup Summary: Highlights from useR! 2014 - Part 2</title><link>https://datasciencela.github.io/la-r-meetup-summary-highlights-from-user-2014-part-2/</link><pubDate>Tue, 18 Nov 2014 09:20:24 -0800</pubDate><guid>https://datasciencela.github.io/la-r-meetup-summary-highlights-from-user-2014-part-2/</guid><description>&lt;p&gt;Last week the &lt;a href="https://datasciencela.github.io/category/r/"&gt;LA R meetup&lt;/a&gt; featured another round of 5 speakers each highlighting a few things they had found interesting at the &lt;a href="http://user2014.stat.ucla.edu/"&gt;useR! 2014 conference&lt;/a&gt;. This was the second such event after the &lt;a href="https://datasciencela.github.io/la-r-meetup-summary-highlights-from-the-user-2014-conference/"&gt;first one in September&lt;/a&gt; - probably because useR! was awesome enough that it inspired 10 volunteers to stand up on stage and talk more about it.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/la-r-meetup-summary-highlights-from-user-2014-part-2/feature.png"/></item><item><title>R in Production - Panel Discussion - LA R meetup @ useR! 2014</title><link>https://datasciencela.github.io/r-in-production-panel-discussion-la-r-meetup-user-2014/</link><pubDate>Thu, 21 Aug 2014 14:27:50 -0700</pubDate><guid>https://datasciencela.github.io/r-in-production-panel-discussion-la-r-meetup-user-2014/</guid><description>&lt;p&gt;&lt;em&gt;(Please click &lt;a href="https://www.youtube.com/watch?v=Izg85RV7OGs"&gt;here&lt;/a&gt; if the video above doesn’t load.)&lt;/em&gt;&lt;/p&gt;&#10;&lt;p&gt;The video above is from an event at &lt;a href="http://user2014.stat.ucla.edu/"&gt;useR! 2014&lt;/a&gt; consisting of &lt;a href="https://datasciencela.github.io/r-in-production-opening-talk-by-yasmin-lucero-la-r-meetup-user-2014/"&gt;Yasmin Lucero’s presentation&lt;/a&gt; plus an incredible panel discussion, which is included here. The title of the panel was “R’s Place in the Production Environment”, and was moderated by &lt;a href="https://www.linkedin.com/in/tareefk"&gt;Tareef Kawaf&lt;/a&gt;, President at &lt;a href="http://www.rstudio.com/"&gt;RStudio&lt;/a&gt;. Five high-profile members of the R community were on this panel, spanning the gamut of the enterprise:&lt;/p&gt;</description></item><item><title>R in Production - Opening Talk by Yasmin Lucero - LA R meetup @ useR! 2014</title><link>https://datasciencela.github.io/r-in-production-opening-talk-by-yasmin-lucero-la-r-meetup-user-2014/</link><pubDate>Thu, 21 Aug 2014 13:54:56 -0700</pubDate><guid>https://datasciencela.github.io/r-in-production-opening-talk-by-yasmin-lucero-la-r-meetup-user-2014/</guid><description>&lt;p&gt;&lt;em&gt;(Please click &lt;a href="https://www.youtube.com/watch?v=O5H6jKyLBqA"&gt;here&lt;/a&gt; if the video above doesn’t load.)&lt;/em&gt;&lt;/p&gt;&#10;&lt;p&gt;During useR! 2014, the &lt;a href="https://datasciencela.github.io/meetups-r/"&gt;LA R Meetup&lt;/a&gt; (run by &lt;a href="https://datasciencela.github.io/team/#szilard"&gt;Szilard Pafka&lt;/a&gt;) organized an event focused on R’s status in industry. This event took the form of a presentation by &lt;a href="http://yasminlucero.net/"&gt;Yasmin Lucero&lt;/a&gt;, Senior Statistician at AOL-Gravity, followed by a &lt;a href="https://datasciencela.github.io/r-in-production-panel-discussion-la-r-meetup-user-2014/"&gt;panel discussion&lt;/a&gt;. The video above captures the presentation, where Yasmin provides an excellent overview of the current status of R in the enterprise and breaks down the taxonomy of solutions for which R is commonly chosen as a development platform. From batch reporting and dashboards for internal deployment to full blown public facing analytics data products, R has certainly found a home in business. Yasmin’s talk concludes with specific, operational aspects of managing R in the enterprise, such as library and dependency management. Enjoy the video and don’t forget to tune in for Part 2 of this session where the panel discussed “R’s Place in the Production Environment.”&lt;/p&gt;</description></item></channel></rss>