<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning on the art of simplicity</title><link>https://naoko.github.io/tags/learning/</link><description>Recent content in Learning on the art of simplicity</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sun, 08 Jul 2018 00:00:00 +0000</lastBuildDate><atom:link href="https://naoko.github.io/tags/learning/index.xml" rel="self" type="application/rss+xml"/><item><title>ML Platforms</title><link>https://naoko.github.io/posts/2018-07-08-ml-platform/</link><pubDate>Sun, 08 Jul 2018 00:00:00 +0000</pubDate><guid>https://naoko.github.io/posts/2018-07-08-ml-platform/</guid><description>&lt;h3 id="the-problem"&gt;The problem:&lt;/h3&gt;
&lt;p&gt;When you start small Machine Learning team with a few projects, your experiment is done
via Jupyter Notebook and maybe the notebook is in github.
The notebook might contain a method to download data so it can be reproducible but
it is getting harder and harder to track various experiments.&lt;/p&gt;
&lt;p&gt;We also need to make sure models does not train on corrupted / skewed data
and only high quality model are pushed to production.
Currently these processes are manual and not centralized nor has unified common tool.&lt;/p&gt;</description></item></channel></rss>