This is a cross-over episode from our new show The Machine Learning Podcast, the show about going from idea to production with machine learning.
SummaryDeep 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-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle.
AnnouncementsThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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Exploring Python's Internals By Rewriting Them In Rust
Version Control For Your Machine Learning Projects
Building Scalable Ecommerce Sites On Saleor
A Quick Python Check-in With Naomi Ceder
Wes McKinney's Career In Python For Data Analysis
The Past, Present, and Future of Deep Learning In PyTorch
How To Include Redis In Your Application Architecture
Marshmallow Data Validation Library
Unpacking The Python Toolkit For Chaos Engineering
Computational Musicology For Python Programmers
Classic Computer Science For Pythonistas
What You Need To Know About Open Source Licenses And Intellectual Property
Counteracting Code Complexity With Wily
Teaching Digital Archaeology With Jupyter Notebooks
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AIORTC: An Asynchronous WebRTC Framework with Jeremy Lainé
Polyglot: Multi-Lingual Natural Language Processing with Rami Al-Rfou
Gnocchi: A Scalable Time Series Database For Your Metrics with Julien Danjou
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