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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Combining Python And SQL To Build A PyData Warehouse
AI Driven Automated Code Review With DeepCode
Security, UX, and Sustainability For The Python Package Index
Learning To Program In Python With CodeGrades
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Docker Best Practices For Python In Production
Protecting The Future Of Python By Hunting Black Swans
A Modern Open Source Project Management Platform
Domain Driven Design For Python
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Behind The Scenes At The Python Software Foundation
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Building A Business On Serverless Technology
A Data Catalog For Your PyData Projects
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Building A Privacy Preserving Voice Assistant
Hacking The Government With The USDS
Probabilistic Modeling In Python (And What That Even Means)
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