59 min

Declarative Machine Learning For High Performance Deep Learning Models With Predibase The Python Podcast.__init__

    • Technology

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

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
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

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

Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great!
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. And now you can launch a managed MySQL, Postgres, or Mongo database cluster in minutes to keep your critical data safe with automated backups and failover. Go to pythonpodcast.com/linode and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
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

59 min

Top Podcasts In Technology

Acquired
Ben Gilbert and David Rosenthal
All-In with Chamath, Jason, Sacks & Friedberg
All-In Podcast, LLC
Search Engine
PJ Vogt, Audacy, Jigsaw
Lex Fridman Podcast
Lex Fridman
Hard Fork
The New York Times
Underserved
Andrew Gelina