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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Practical Advice On Using Python To Power A Business
Analyzing The Ecosystem of Python Data Companies With Tony Liu
Go From Notebook To Pipeline For Your Data Science Projects With Orchest
Write Your Python Scripts In A Flow Based Visual Editor With Ryven
CrossHair: Your Automatic Pair Programmer
Giving Your Data Science Projects And Teams A Home At DagsHub
Exploring Literate Programming For Python Projects With nbdev
Making The Sans I/O Ideal A Reality For The Websockets Library
Driving Toward A Faster Python Interpreter With Pyston
Project Scaffolding That Evolves With Your Software Using Copier
How Python's Evolution Impacts Your Fluency With Luciano Ramalho
Making Content Management A Smooth Experience With A Headless CMS
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Add Anomaly Detection To Your Time Series Data With Luminaire
Building Big Data Pipelines For Audio With Klio
Open Sourcing The Anvil Full Stack Python Web App Platform
Pants Has Got Your Python Monorepo Covered
Scale Your Data Science Teams With Machine Learning Operations Principles
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