This chapter explores the practice of modeling in the machine learning ecosystem and how it has evolved over time. It discusses the adoption of real-time data in machine learning and the shift from machine learning as an analytics function to machine learning being the product. The chapter also discusses the advantages, risks, and necessary skills for teams to enter the modern era of model building in machine learning.
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- What are some of the practices that are still necessary/useful and what is becoming outdated?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?
Contact Info
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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Links
The intro and outro music is from
Hitman's Lovesong feat. Paola Graziano by
The Freak Fandango Orchestra/
CC BY-SA 3.0
