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<feed xmlns="http://www.w3.org/2005/Atom"><title>PyVideo.org - Chris Menezes</title><link href="https://pyvideo.org/" rel="alternate"></link><link href="https://pyvideo.org/feeds/speaker_chris-menezes.atom.xml" rel="self"></link><id>https://pyvideo.org/</id><updated>2018-11-10T00:00:00+00:00</updated><subtitle></subtitle><entry><title>Data Science, from Concept to Production</title><link href="https://pyvideo.org/pycon-ca-2018/data-science-from-concept-to-production.html" rel="alternate"></link><published>2018-11-10T00:00:00+00:00</published><updated>2018-11-10T00:00:00+00:00</updated><author><name>Chris Menezes</name></author><id>tag:pyvideo.org,2018-11-10:/pycon-ca-2018/data-science-from-concept-to-production.html</id><summary type="html">&lt;h3&gt;Description&lt;/h3&gt;&lt;p&gt;So you've got an idea for a machine learning product, but how do you actually get it to production? From going on-call for ML models, to ensuring that models built by your data scientists can be used by your engineers, join me for a fast paced guide to the …&lt;/p&gt;</summary><content type="html">&lt;h3&gt;Description&lt;/h3&gt;&lt;p&gt;So you've got an idea for a machine learning product, but how do you actually get it to production? From going on-call for ML models, to ensuring that models built by your data scientists can be used by your engineers, join me for a fast paced guide to the world of data science in production.&lt;/p&gt;
&lt;p&gt;Presentation page -- &lt;a class="reference external" href="https://2018.pycon.ca/talks/talk-PC-55546/"&gt;https://2018.pycon.ca/talks/talk-PC-55546/&lt;/a&gt;
Author website -- &lt;a class="reference external" href="https://www.pagerduty.com/"&gt;https://www.pagerduty.com/&lt;/a&gt;&lt;/p&gt;
</content><category term="PyCon CA 2018"></category></entry></feed>