<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Scaling on Harlan D. Harris</title><link>https://harlanh.tech/tags/scaling/</link><description>Recent content in Scaling on Harlan D. Harris</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>harlan@harris.name (Harlan Harris)</managingEditor><webMaster>harlan@harris.name (Harlan Harris)</webMaster><lastBuildDate>Fri, 09 Jun 2017 00:00:00 +0000</lastBuildDate><atom:link href="https://harlanh.tech/tags/scaling/index.xml" rel="self" type="application/rss+xml"/><item><title>What do Data Scientists mean by “Scaling”?</title><link>https://harlanh.tech/2017/06/what-do-data-scientists-mean-by-scaling/</link><pubDate>Fri, 09 Jun 2017 00:00:00 +0000</pubDate><author>harlan@harris.name (Harlan Harris)</author><guid>https://harlanh.tech/2017/06/what-do-data-scientists-mean-by-scaling/</guid><description>
&lt;p&gt;&lt;em&gt;&lt;a href="https://medium.com/@HarlanH/what-do-data-scientists-mean-by-scaling-49e89c954d4"&gt;This post was originally published on Medium&lt;/a&gt;&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;Occasionally when chatting with other data scientists, especially with others who are interested in integrating predictive models into production software system, the word “scaling” comes up.&lt;/p&gt;
&lt;p&gt;&lt;img src="https://cdn-images-1.medium.com/max/2000/1*n0gZKfjQ4SPOYJFOHMo1UA.jpeg" alt="Not this. Although some West Coast data scientists are into this kind of scaling too." /&gt;&lt;em&gt;Not this. Although some West Coast data scientists are into this kind of scaling too.&lt;/em&gt;&lt;/p&gt;
&lt;p&gt;I think this is a great question, but it’s a little underspecified. There seem to be at least three qualitatively different notions of “scaling” in data science, and it’s worth the effort to clarify each of them, and address how people tackle them.&lt;/p&gt;</description></item></channel></rss>