Declarative Machine Learning For High Performance Deep Learning Models With Predibase

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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.
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-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.
The 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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Introduction and Episode Overview
Interview with Travis Adaire: Introduction and Background
Predibase: Concept and Development
Target Audience and Use Cases
Understanding Machine Learning Problems
Declarative ML vs. AutoML
System Design and Architecture
Evolution and Customer Feedback
Workflow and Integration
PQL: Predictive Query Language
Collaboration and User Roles
Open Source and Ludwig
Customer Use Cases and Feedback
Lessons Learned and Challenges
Testing, Validation, and Stability
When Predibase is Not the Right Choice
Future Plans and Features
Biggest Barriers to ML Adoption
Conclusion and Closing Remarks
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