The Role Of Model Development In Machine Learning Systems

May 29, 2023

Josh Tobin discusses the shift in focus from model development to machine learning systems, the evolution of modeling in the machine learning ecosystem, the capabilities of Gantry in enhancing model performance and maintenance, core capabilities and flexible support for machine learning, innovative approaches and challenges in building and deploying machine learning models, and when to choose Gantry for model development and maintenance.

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Introduction

00:00 • 2min

Modeling in the Machine Learning Ecosystem

01:31 • 25min

Gantry: Enhancing Model Performance and Maintenance

26:04 • 12min

Core Capabilities and Flexible Support for Machine Learning

37:46 • 2min

Innovative Approaches and Challenges in Building and Deploying Machine Learning Models

39:30 • 2min

When to Choose Gantry for Model Development and Maintenance

41:51 • 3min

Wrapping up the episode and discussing the biggest barrier to adoption for machine learning

44:22 • 2min

Summary
The focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.
Announcements

Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Josh Tobin about the state of industry best practices for designing and building ML models

Interview

Introduction
How did you get involved in machine learning?
Can you start by describing what a "traditional" process for building a model looks like?
- What are the forces that shaped those "best practices"?
What are some of the practices that are still necessary/useful and what is becoming outdated?
- What are the changes in the ecosystem (tooling, research, communal knowledge, etc.) that are forcing teams to reconsider how they think about modeling?
What are the most critical practices/capabilities for teams who are building services powered by ML/AI?
- What systems do they need to support them in those efforts?
Can you describe what you are building at Gantry and how it aids in the process of developing/deploying/maintaining models with "modern" workflows?
What are the most challenging aspects of building a platform that supports ML teams in their workflows?
What are the most interesting, innovative, or unexpected ways that you have seen teams approach model development/validation?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gantry?
When is Gantry the wrong choice?
What are some of the resources that you find most helpful to stay apprised of how modeling and ML practices are evolving?

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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. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0