Declarative Machine Learning For High Performance Deep Learning Models With Predibase

<div class="wp-block-jetpack-markdown"><h2>Preamble</h2> <p>This is a <a href="https://www.themachinelearningpodcast.com/predibase-declarative-machine-learning-episode-4/?utm_source=rss&utm_medium=rss" target="_blank" rel="noopener">cross-over episode</a> from our new show <a href="https://www.themachinelearningpodcast.com?utm_source=rss&utm_medium=rss" target="_blank" rel="noopener">The Machine Learning Podcast</a>, the show about going from idea to production with machine learning.</p> <h2>Summary</h2> <p>Deep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-fo...