Accelerate Your Machine Learning Experimentation With Automatic Checkpoints Using FLOR

<div class="wp-block-jetpack-markdown"><h2>Summary</h2> <p>The experimentation phase of building a machine learning model requires a lot of trial and error. One of the limiting factors of how many experiments you can try is the length of time required to train the model which can be on the order of days or weeks. To reduce the time required to test different iterations Rolando Garcia Sanchez created FLOR which is a library that automatically checkpoints training epochs and instruments your code so that you can bypass early training cycles when you want to explore a different path in your algorithm. In this episode he explains how the tool works to speed up your experimentation phase and how to get started with it.</p> <h2>Announcements</h2> <ul> <li>Hello and welcome to Podcast.__init__, the podcast about Python&#8217;s role in data and science.</li> <li>When you&#8217;re ready to launch your next app or want to try a project you hear about on the show, you&#8217;ll need somewhere...