December 05, 2022

Declarative Machine Learning For High Performance Deep Learning Models With Predibase

59 minutes

Preamble

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.

Summary

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.

Announcements

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Your host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative format

Interview

Introduction

How did you get involved in machine learning?

Can you describe what Predibase is and the story behind it?

Who is your target audience and how does that focus influence your user experience and feature development priorities?

How would you describe the semantic differences between your chosen terminology of "declarative ML" and the "autoML" nomenclature that many projects and products have adopted?

Another platform that launched recently with a promise of "declarative ML" is Continual. How would you characterize your relative strengths?

Can you describe how the Predibase platform is implemented?

How have the design and goals of the product changed as you worked through the initial implementation and started working with early customers?

The operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery?

Ludwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product?

In addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision?

How did you approach the semantic and syntactic design of the dialect?

What is your vision for PQL in the space of "declarative ML" that you are working to define?

Can you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model?

Once a model has been deemed satisfactory, what is the path to production?

How are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase?

What are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase?

What are the most interesting, innovative, or unexpected ways that you have seen Predibase used?

What are the most interesting, unexpected, or challenging lessons that you have learned while working on Predibase?

When is Predibase the wrong choice?

What do you have planned for the future of Predibase?

Contact Info

tgaddair on GitHub

@travisaddair on Twitter

Parting Question

From your perspective, what is the biggest barrier to adoption of machine learning today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.

Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.

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Links

Predibase

Horovod

Ludwig

Podcast.__init__ Episode

Support Vector Machine

Hadoop

Tensorflow

Uber Michaelangelo

AutoML

Spark ML Lib

Deep Learning

PyTorch

Continual

Data Engineering Podcast Episode

Overton

Kubernetes

Ray

Nvidia Triton

Whylogs

Data Engineering Podcast Episode

Weights and Biases

MLFlow

Comet

Confusion Matrices

dbt

Data Engineering Podcast Episode

Torchscript

Self-supervised Learning

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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By Tobias Macey

4.4

100100 ratings