The Future of Machine Learning Lies in Better Abstractions

This week’s guest is Travis Addair, he previously led the team at Uber that was responsible for building Uber’s deep learning infrastructure. Travis is deeply involved with two popular open source projects related to deep learning:He is maintainer of Horovod, a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.And Travis is a co-maintainer of Ludwig, a toolbox that allows users to train and test deep learning models without the need to write code.Subscribe: Apple • Android • Spotify • Stitcher • Google • RSS.Detailed show notes can be found on The Data Exchange web site.Subscribe to The Gradient Flow Newsletter.

Om Podcasten

A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].